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Record: 1- A new decisional balance measure of motivation to change among at-risk college drinkers. Collins, Susan E.; Carey, Kate B.; Otto, Jacqueline M.; Psychology of Addictive Behaviors, Vol 23(3), Sep, 2009 pp. 464-471. Publisher: American Psychological Association; [Journal Article] Abstract: In this study, an open-ended decisional balance worksheet was used to elicit self-generated pros and cons of current drinking and reducing drinking, which were then quantified to create a decisional balance proportion (DBP) reflecting movement toward change (i.e., counts of pros of reducing drinking and cons of current drinking to all decisional balance fields). This study’s goal was to examine the convergent, discriminant, and predictive validity of the DBP as a measure of motivation to change. Participants were college students (N = 143) who reported having engaged in weekly heavy, episodic drinking and who had participated in a larger randomized clinical trial of brief motivational interventions (K. B. Carey, M. P. Carey, S. A. Maisto, & J. M. Henson, 2006). Findings indicated partial support for convergent and discriminant validity of the DBP. Compared with Likert scale measures of decisional balance and readiness to change, DBP scores reflecting greater movement toward change best predicted reductions in heavy drinking quantity and frequency and experience of alcohol-related consequences, although some of these effects decayed by the 12-month follow-up. Findings suggest that the DBP is a valid measure of motivation to change among at-risk college drinkers. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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A New Decisional Balance Measure of Motivation to Change Among At-Risk College Drinkers
By: Susan E. Collins
Addictive Behaviors Research Center, University of Washington;
Kate B. Carey
Center for Health and Behavior, Syracuse University
Jacqueline M. Otto
Addictive Behaviors Research Center, University of Washington
Acknowledgement: Susan E. Collins’s time was supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Institutional Training Grant T32AA007455 awarded to Mary E. Larimer at the University of Washington. This study was supported in part by NIAAA Grant AA12518 awarded to Kate B. Carey.
A big thanks goes to Dan J. Neal for his statistical insights and advice, to Carrie Luteran for assembling the data sets, and Sonia Kaur for her help with data entry. Thanks also to Sandra Eck for the helpful ongoing discussions about decisional balance measurement in new participant populations.
Decision making often involves consideration of a set of possible behavioral options and an evaluation of the consequences of each option. These consequences may range from desirable to undesirable effects, also referred to as pros and cons (Janis & Mann, 1977). A decisional balance has thus been operationalized as a representation of the pros and cons of a certain behavior and its potential alternatives.
The explicit consideration of the decisional balance—either as a written or counselor-facilitated exercise—was originally proposed to reduce decision-making errors by making people more cognizant of the decision-making process and the factors contributing to their decisions (Janis, 1968). In this context, decisional balance was designed as a therapeutic exercise to facilitate the complete and realistic assessment of the net value of a potential behavior. As this technique has evolved over time, clinicians have started to employ guided decisional balance exercises as a means of enhancing motivation to change risky health behaviors (Dimeff, Baer, Kivlahan, & Marlatt, 1999; Miller, 1999). Specifically, clients articulate and examine ambivalence about their current behavior to determine whether the weight of the evidence is accumulating toward the need for behavior change (Miller, 1999). In the college drinking literature, a limited number of studies have used a guided, open-ended decisional balance as an intervention tool, but the results of guided interventions have been mixed with regard to alcohol use outcomes (Carey, Carey, Maisto, & Henson, 2006; S. E. Collins & Carey, 2005; LaBrie, Pedersen, Earlywine, & Olsen, 2006).
Alternatively, the data generated in a decisional balance could reflect resolve to enter into a course of action (Janis & Mann, 1977), thus serving as a proxy for motivation to change. This idea was echoed in the work of DiClemente and colleagues (e.g., DiClemente et al., 1991), who have since recognized decisional balance as a marker for the initiation of different stages of change (Pollak, Carbonari, DiClemente, Niemann, & Mullen, 1998). In fact, the stages of change have been operationalized algorithmically as a function of change in the decisional balance (Hall & Rossi, 2008; Prochaska, 1994). Thus, the historical development of the decisional balance construct shows its potential to both enhance and reflect people’s motivational states. In this study, we focused on the role of decisional balance as an assessment tool rather than as an intervention procedure.
The existence of a relationship between decisional balance and motivation to change is evident; however, the nature of this relationship has been described differently in different theoretical contexts. Furthermore, how decisional balance is measured has included the consideration of the pros and cons of a behavior or the pros and cons of an alternative behavior. What exactly decisional balance measures, however, has been less clearly defined. For example, some researchers have asserted that it represents the decision-making process itself (Fischoff & Quadrel, 1991; Janis & Mann, 1977), others have posited that it represents one dimension of motivation to change (Miller, 1999), and yet others have suggested that it is a covariate or perhaps a mechanism involved in transitioning through various stages of behavior change (Prochaska et al., 1994). Considering the ambiguity in the relationship between motivation and decisional balance, more research is needed to establish the validity and clinical utility of decisional balance as a measure of motivation to change.
Decisional Balance MeasuresReflecting the ambiguity surrounding the decisional balance construct, several decisional balance measures for drinking have been designed over the past 2 decades. The Alcohol Decisional Balance Scale assesses the pros and cons of maintaining one’s current alcohol use using a 42-item Likert scale questionnaire (King & DiClemente, 1993). The Alcohol and Drug Consequences Questionnaire is a 28-item, 6-point Likert scale questionnaire designed to assess the pros and cons of changing alcohol or drug behavior (Cunningham, Gavin, Sobell, Sobell, & Breslin, 1997). Finally, the Decisional Balance for Immoderate Drinking (DBID) was developed for college students (Migneault, Velicer, Prochaska, & Stevenson, 1999). This measure consists of 25 Likert scale questions assessing on a 5-point scale the importance of selected pros and cons of “immoderate” drinking. Different sets of these original 25 items may be summed to form either two 20-item scales (pros vs. cons of immoderate drinking) or three 18-item scales (pros, potential cons, and actual cons).
Despite initially promising psychometric evaluations of these questionnaires, conceptual weaknesses can be identified. First, all of these measures have focused exclusively on the pros and cons of current drinking or the pros and cons of reducing or changing drinking, which precludes the evaluation of the decisional balance as a whole. An incomplete decisional balance has been viewed as problematic in decision-making theory because of the potential for overlooked consequences to create new ambivalence after a decision has been reached (Janis & Mann, 1977). Similarly, an incomplete measure of decisional balance may fail to take into account all aspects of a person’s current motivation to change. One empirical study found that consideration of both the target behavior and an alternative behavior nearly doubled the number of pros and cons spontaneously produced across multiple risky behaviors (Beyth-Marom, Austin, Fischhoff, Palmgren, & Jacobs-Quadrel, 1992). Particularly among adolescents in that study, negative social consequences were reported more often in assessing the cons of not drinking. Thus, a focus on only one half of the decision-making process (i.e., either the pros and cons of current or alternative behavior) might lead to an incomplete and potentially less predictive measure of decisional balance.
Another problem with decisional balance measures to date is that the pros and cons are generated by researchers instead of by participants themselves. This approach may be inadequate in capturing motivation to change authentically and accurately. First, if researchers approach the topic from an academic perspective, they may identify different pros and cons of current drinking versus drinking behavior change than participants (Fischoff & Quadrel, 1991). Also, the language used by researchers to describe the pros and cons can, in and of itself, influences participants’ interpretation of the item and thus the participants’ answers (Beyth-Marom et al., 1992; Fischoff & Quadrel, 1991). On a related point, in providing participants with the “correct” pros and cons of drinking, researchers may be artificially constructing the decision-making process to which participants passively respond. This approach may have the unwanted side effect of making respondents aware of pros and cons they may not have otherwise considered and that may not represent their own unique decision-making process. In contrast, use of an open-ended response format allows participants to express their actual motivational state rather than respond to researchers’ perspectives and values (Fischoff & Quadrel, 1991).
In light of these concerns, it is plausible that an open-ended, participant-generated decisional balance could provide a more accurate measure of motivation to change and better predict drinking outcomes among college drinkers. In an exploratory analysis, Collins and Carey (2005) presented some evidence that, when used as an assessment of motivation, the pattern of responses generated during a decisional balance exercise predicted drinking outcomes with modest success. Specifically, among participants receiving an in-person decisional balance exercise, a greater proportion of pros to cons of changing one’s drinking predicted short-term drinking outcomes.
Current StudyThe current study was designed to extend these preliminary findings and examine an expanded, open-ended, four-field decisional balance worksheet as a measure of motivation to change drinking among at-risk college drinkers. The decisional balance worksheet prompted respondents to report the pros and cons of their current drinking versus reduced drinking. Next, the number of pros and cons reported in each field was converted into a proportion representing the decisional balance toward change or the decisional balance proportion (DBP).
The goal of this study was to test the validity of the DBP as a new measure of motivation to change drinking. We hypothesized that the DBP would evince convergent validity by positively correlating with an alternative, Likert scale measure of readiness to change, and positively correlating with the cons and negatively correlating with the pros of drinking as measured by continuous decisional balance scales. Furthermore, we hypothesized that the DBP would evince discriminant validity by showing nonsignificant correlations with measures assessing dissimilar constructs (i.e., social desirability and demographic variables). Finally, we hypothesized that DBP change scores would evince predictive validity. Initial increases in the decisional balance reflecting movement toward drinking behavior change would predict greater decreases in heavy drinking indices over the follow-up period compared with a DBP reflecting no movement or movement away from change.
Method Participants
Participants consisted of 143 undergraduate volunteers who had participated in a randomized clinical trial of two types of brief motivational interventions (see Carey et al., 2006). Inclusion criteria for this trial were (a) reporting at least one heavy drinking episode in an average week or at least four heavy drinking episodes in the past month, (b) being 18–25 years of age, (c) being a freshman, sophomore, or junior in college, and (d) consenting to participate. Only students who had participated in the first year of the larger study were included in the current secondary analyses because the format of the original measure was changed after the first year. The new format allowed for electronic data scanning but, as a consequence, limited the number of potential entries. Because the number of entries in each field of the decisional balance was the primary focus of the current study, the data set includes only the participants who had the opportunity to respond to the unrestricted format.
The subsample of 143 students in the current study was predominately female (68%, n = 97), and the average age was 19.20 years (SD = 0.87). The sample consisted of 65% freshmen, 27% sophomores, and 7% juniors. The majority self-identified as White (87%), whereas 2% self-identified as Black/African American, 5% as Asian/Pacific Islander, 3% as Hispanic/Latino/a, and 4% as other or multiracial. Membership in a fraternity or sorority was reported by 24% of the sample, and most participants reported living on campus (89%) or in a fraternity or sorority house (2%), as opposed to off campus (8%).
Measures
A set of demographic questions assessed participants’ age, gender, year in college, ethnicity, on- or off-campus residence, and membership in an on-campus Greek organization. Social desirability was measured using a 13-item short form of the Marlowe Crowne Social Desirability Scale (Reynolds, 1982). The alpha in the current sample was adequate (α = .63).
The decisional balance worksheet was modeled after a scale used to assess the accessibility of alcohol expectancies (see Stacy, Leigh, & Weingardt, 1994). Because it is a free-recall task, it was administered prior to other Likert scale measures of decisional balance. Participants recorded each “advantage” and “disadvantage” of “continuing to drink as you are now” and “drinking less than you do now” on prenumbered lines on the open-ended decisional balance worksheet. The counts of the pros and cons were obtained by summing the prenumbered lines filled in by participants and formed the main explanatory variable for this study, DBP, which may be written as
where subscripts red = reducing drinking and cur = current drinking. DBP scores at 0.5 represent an even balance between pros and cons of reducing drinking and current drinking. Scores between 0.5 and 1.0 indicate a balance tipped toward reducing drinking, and DBP scores between 0.0 and 0.5 indicate a balance tipped toward maintenance of current drinking. In the current study, baseline DBP was used in the convergent and discriminant validity tests, whereas change in the DBP from baseline to 1-month follow-up was used in predictive validity tests.
The 12-item Readiness to Change Questionnaire (RTCQ; Rollnick, Heather, Gold, & Hall, 1992) was scored as a continuous measure of readiness to change (Budd & Rollnick, 1996). The RTCQ was used in convergent and predictive validity analyses in the current study. The alpha reached an adequate level of item consistency (α = .80).
The DBID (Migneault et al., 1999) is a 25-item Likert scale measure developed for college students. Different sets of these original 25 items may be summed to form either two scales (10-item Pros vs. 10-item Cons of immoderate drinking) or three scales (10-item Pros, 3-item Potential Cons, and 5-item Actual Cons). The 18-item, three-factor solution was used in tests of convergent and predictive validity in the current study. In the overall sample, reliability was calculated for Pros (α = .81), Potential Cons (α = .71), and Actual Cons (α = .52).
All drinking assessments used the previous 30 days as a uniform time frame and defined a drink as a 10- to 12-oz can or bottle of 4%–5% beer, a 4-oz glass of 12% table wine, a 12-oz bottle or can of wine cooler, or a 1.25-oz shot of 80-proof liquor either straight or in a mixed drink. A modified version of the Daily Drinking Questionnaire (R. L. Collins, Parks, & Marlatt, 1985) allowed for calculation of drinking frequency and quantity per heaviest drinking week. Using this measure, participants also estimated the frequency of heavy episodic drinking (HED), defined as five or more drinks for men and four or more drinks for women on one occasion (Wechsler et al., 2002). This measure yielded three of the alcohol outcome variables.
The Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989) consists of 23 items assessing alcohol-related problems and was specifically developed for use with adolescents and young adults. Participants used a Likert scale to indicate how many times in the past 30 days they experienced each problem listed (i.e., 0 = 0 times, 1 = 1–2 times, 2 = 3–5 times, 3 = 6–10 times, 4 = more than 10 times), and a summary score represented severity of problems. Adequate internal consistency was obtained in the overall sample (α = .82).
Procedure
Participants attending group baseline sessions provided informed consent and completed the measures mentioned above; all received course credit for their participation. Those who reported engaging in HED at least 4 times in the past month were invited via telephone to participate in a randomized clinical trial that included various brief intervention and assessment conditions (for details, see Carey et al., 2006). Following the intervention period, participants were invited to attend 1-, 6-, and 12-month in-person follow-up assessments for which they filled out the same questionnaires and received course credit, $20, and $25, respectively.
Results Preliminary Data Analysis
The distributions of the explanatory and outcome variables were examined for univariate outliers and deviation from the expected distributions. All drinking outcome measures were positively skewed count variables that approximated the negative binomial distribution. There were no extreme univariate outliers (see Table 1 for all means and standard deviations).
Descriptive Statistics for Explanatory and Response Variables (N = 143)
Convergent Validity
To determine the convergent validity of the DBP, we conducted bivariate Spearman correlations between the baseline DBP and baseline summary scores for the RTCQ, DBID–Pros, DBID–Potential Cons, and DBID–Actual Cons. Although the correlations with the DBP were all in the expected directions, only the correlations between the DBP and RTCQ and DBID–Actual cons were statistically significant (see Table 2 for correlations).
Bivariate Correlations Between the DBP, RTCQ, and DBID Scales
Discriminant Validity
Bivariate Spearman correlations and Mann–Whitney U tests were conducted to test the hypothesized lack of association between the baseline DBP and gender, U(N = 143) = –0.26, p = .80; race/ethnicity, U(N = 143) = –1.32, p = .19; housing situation, U(N = 142) = 0.78, p = .44; social desirability, ρ = –0.09, p = .28; age, ρ = –0.12, p = .14; and Greek membership, U(N = 143) = –1.03, p = .30. As all tests were nonsignificant, discriminant validity of this measure was supported.
Predictive Validity of Change in Decisional Balance
Analysis plan
Population-averaged generalized estimating equation (Zeger & Liang, 1986) models were conducted using STATA 10 (StataCorp, 2007) and tested the change in DBP from baseline to 1-month follow-up as a predictor of heavy drinking outcomes over the 12-month follow-up period. For those unfamiliar with population-averaged generalized estimating equation models, they may be conceptualized as marginal regression models that can be applied to data conforming to different types of distributions (e.g., normal, Poisson, negative binomial, binomial) and can take into account nonindependence resulting from data clustering (e.g., longitudinal data collected on one participant).
The outcome variables were based on a time frame of the previous 30 days and included drinking quantity and frequency during the heaviest drinking week, HED frequency, and RAPI score. Because the distributions of the drinking outcome variables were positively skewed, and the variables were typically overdispersed count/integer responses (e.g., number of drinks consumed during the heaviest drinking week), negative binomial distributions were specified (cf. Neal & Simons, 2007). To enhance interpretability of the regression coefficients, we used the log link for these models. For all variables, repeated measures on one case served as the clustering variable. Because the drinking outcome variables were longitudinal, unevenly spaced, and variably correlated, we used an unspecified correlation structure (Hardin & Hilbe, 2003). The unspecified correlation structure allowed for the correlation in the drinking outcome data at each time point to be taken into account in the overall model estimation.
Three separate models for each of the drinking outcome variables were used to test the relative predictive abilities of DBP, RTCQ, and DBID change scores. The DBP models included five predictors: a linear time variable that compared drinking outcomes at baseline, 1-, 6-, and 12-month follow-ups (coded as 0, 1, 6, and 12, respectively); a quadratic time variable, which took into account the fact that alcohol use over time often follows a curvilinear versus a straight linear path; the DBP change score, which reflected movement in the balance toward or away from change (1-month follow-up minus baseline DBP); and both Linear and Quadratic Time × DBP interactions. Similar but nonnested models involving change on the RTCQ scale and on the three DBID scales were also run and were subsequently compared on goodness of fit using the quasi-likelihood under the independence model information criterion (QICu; Hardin & Hilbe, 2003). Similar to the Akaike’s information criterion tests, statistically superior models have the lowest QICu scores (Hardin & Hilbe, 2003).
Quantity: Heaviest drinking week
The DBP model (QICu = 5124.78) for quantity during the heaviest drinking week was significant, Wald χ2(5, N = 143) = 14.16, p = .01, and statistically superior to both the RTCQ (QICu = 14458.37) and DBID (QICu = 17711.27) models. After controlling for time and baseline DBP, there were significant Linear Time × DPB (IRR = 0.78, SE = 0.07, p = .006) and Quadratic Time × DBP (IRR = 1.02, SE = 0.007, p = .007) interactions. As shown in Figure 1, all participants seemed to decrease their heavy drinking quantity between baseline and 1-month follow-up, possibly as a result of assessment reactivity. However, increases in DBP scores over the initial 1-month period predicted subsequent decreases in drinking quantity at the 6-month follow-up, an effect that decayed by the 12-month follow-up. Decreasing DBP scores tended to predict an increase in drinking quantity, which was followed by a reduction at the 12-month follow-up. On the other hand, relatively stable DBPs were associated with little change in drinking quantity.
Figure 1. Graph of mean quantity per heaviest drinking week by time point and level of change in decisional balance proportion (DBP). The DBP change scores represent change in the DBP between baseline and the 1-month follow-up. For clarity of presentation, groups were formed to represent different levels of change in the DBP. The stable DBP group in the figure is centered on the mean DBP change score (M = –0.004, SD = 0.20) in this sample and includes difference scores ranging from –0.20 to 0.20. These scores correspond to 1 SD below and 1 SD above the mean, respectively. The decreasing DBP group represents participants whose DBP change scores were at least 1 SD below the mean (DBP < –0.20), and the increasing DBP group represents participants whose DBP change scores were at least 1 SD above the mean (DBP > 0.20).
Frequency: Heaviest drinking week
The DBP model (QICu = 343.23) for frequency during the heaviest drinking week was statistically superior to both the RTCQ (QICu = 1100.58) and DBID (QICu = 1280.73) models, and was significant, Wald χ2(5, N = 143) = 15.36, p = .009. After controlling for time and baseline DBP, there were significant Linear Time × DPB (IRR = 0.84, SE = 0.05, p = .003) and Quadratic Time × DBP (IRR = 1.01, SE = 0.005, p = .005) interactions. After all groups initially decreased on heavy drinking frequency, increasing DBP over the initial 1-month period predicted greater decreases in drinking frequency during the heaviest drinking week—until the 12-month follow-up, when this decreasing effect decayed (see Figure 2). Decreasing DBP scores tended to predict increases in drinking frequency up to the 6-month time point, followed by a reduction at the 12-month follow-up. On the other hand, relatively stable DBPs were associated with little change in drinking frequency.
Figure 2. Graph of mean frequency per heaviest drinking week by time point and level of change in decisional balance proportion (DBP).
HED
The best model according to the QICu was the DPB model (QICu = 1481.03) compared with the RTCQ (QICu = 4069.71) and DBID (QICu = 4891.80) models; however, none of the omnibus model tests for HED outcomes were significant (all ps > .18).
RAPI
Compared with the RTCQ (QICu = 5375.68) and DBID (QICu = 6493.62) models, the DBP (QICu = 1975.94) model provided the best fit for self-reported alcohol-related problems, Wald χ2(5, N = 143) = 13.12, p = .02. After controlling for time and baseline DBP, there was a significant Linear Time × DPB interaction (IRR = 0.85, SE= 0.07, p = .046). As shown in Figure 3, all participants reported initial decreases in RAPI. Increases in DBP over the initial 1-month period, however, predicted a stable linear decrease in alcohol-related problems over the follow-up period. On the other hand, decreasing DBP scores tended to predict increases in alcohol-related problems up to the 6-month time point, followed by a downward trend at the 12-month follow-up. On the other hand, relatively stable DBPs are associated with relatively stable experience of alcohol-related problems.
Figure 3. Graph of mean alcohol-related problems (Rutgers Alcohol Problem Index [RAPI] score) by time point and level of change in decisional balance proportion (DBP).
DiscussionThis study provided an examination of a DBP as a measure of motivation to change among at-risk college drinkers. The DBP was generated from responses to an open-ended decisional balance worksheet assessing pros and cons of current drinking versus reducing drinking; it was constructed to reflect the extent to which the decisional balance was tipped toward change.
Convergent validity of the DBP was partially supported in this study. As predicted, initial DBP positively and significantly correlated with readiness to change as measured by the RTCQ. This finding provided convergent validity for the DBP as a measure of motivation to change. Furthermore, the weighted importance of current negative outcomes (DBID–Actual Cons) in participants’ decisions to drink was significantly, albeit weakly, associated with DBP scores. The somewhat weak effect may indicate that the DBID and the DBP measure overlapping yet distinct constructs, or it may reflect the relatively low reliability of the DBID–Actual Cons scale. The latter point is a psychometric issue that may have limited the power to optimally assess convergent validity with this scale.
The DBP was not significantly correlated with the pros scale of the DBID, which taps into the importance of positive aspects of “immoderate” drinking (e.g., “I feel happier when I drink”). However, this nonsignificant correlation is understandable: The DBP was constructed to reflect the tilt of the decisional balance toward change, not the status quo. In fact, the DBP represents the weight of the cons of current drinking plus the pros of changing relative to all fields in the balance. Because this proportion does not explicitly highlight the pros of current drinking, they may be “passively” outweighed.
The fact that the DBP was not correlated with the DBID–Potential Cons scale reflects the mixed findings regarding convergent validity of the DBP. However, this lack of association may also be interpreted in the context of the cognitive and memory literature, which asserts that individual drinking experience influences the accessibility of certain thoughts about alcohol use. Specifically, frequently encountered outcomes, such as those represented by the DBID–Actual Cons scale (e.g., “Drinking makes me feel out of control”), may be more accessible than hypothetical, potential outcomes, such as those measured by the DBID–Potential Cons scale (e.g., “Drinking could kill me”; Stacy et al., 1994). On the other hand, the DBP, which reflects an open-ended assessment of the decisional balance, may be even more accurate in assessing the most personally salient pros and cons of a behavior and may be more reflective of an individual’s current motivational state than the DBID scales.
Evidence for the discriminant validity of the DBP was obtained. Our findings confirmed that the DBP did not significantly correlate with demographic measures (i.e., gender, race/ethnicity, year in college, Greek membership, age) or social desirability. Perusal of the recent literature reveals no studies involving college students that have demonstrated associations between motivation and basic demographic variables or social desirability. Thus, consistent with empirical precedent and theory, the DBP appears to be an independent construct that may be used with a range of college students.
Findings in this study revealed that changes in the DBP predicted heavy drinking outcomes among at-risk college drinkers. In fact, the DBP models fit the data better than did models including RTCQ and DBID change scores as predictors of alcohol use over time. Furthermore, the DBP models were the only ones that yielded consistently significant predictors of drinking outcomes. Across three of the four drinking outcome variables, movement toward change (i.e., an increase in proportion of pros of reduced drinking and cons of current drinking to total item count from baseline to 1-month follow-up) predicted reductions in drinking over the initial follow-up period.
However, this finding was tempered by a curvilinear effect on two of the four drinking outcome variables, drinking quantity and frequency, which complicated the initial linear findings. After participants with increasing motivation to change initially decreased their heavy drinking quantity and frequency (and vice versa), there was an apparent decay in this effect at the 12-month follow-up. This regression to the mean, however, does not necessarily indicate that the DBP does not predict drinking as hypothesized. It is highly possible that the relatively short-term changes in the DBP that occurred initially from baseline to 1-month posttest may be most helpful in predicting more proximal changes in drinking. Thus, the more distal drinking measured 1 year after the initial motivation ratings may not be as reliably predicted by the DBP. This explanation fits with the literature on motivation, which suggests that motivation to change drinking is a fluid state rather than a stable trait (Miller, 1999). It also corresponds to the developmental literature that documents temporal variation in college student drinking patterns over weeks and years (Del Boca, Darkes, Greenbaum, & Goldman, 2004; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996). Further studies are needed to establish the temporal robustness of changes in motivation as measured by the DBP. Perhaps models assessing the time-varying and parallel change in DBP and drinking would be a helpful next step.
Conceptually, the DBP corresponds to decisional balance and motivation to change theory better than the other measures (i.e., RTCQ and DBID) tested as predictors in this study. Unlike previous studies involving decisional balance measures (e.g., Cunningham et al., 1997; King & DiClemente, 1993; Migneault et al., 1999; Velicer, DiClemente, Prochaska, & Brandenburg, 1985), the DBP integrates all four fields of the decisional balance: the pros and cons of both current drinking and drinking reduction. Because both theory and empirical findings have indicated that the consideration of the pros and cons of both current behavior as well as behavior change are key to accurate assessment of a person’s current motivation to change (Beyth-Marom et al., 1992; Janis & Mann, 1977), the use of the decisional balance worksheet may represent a step forward in decisional balance measurement. Furthermore, the fact that the input for the DBP is participant- instead of researcher-generated may make this measure a more accurate and personally relevant representation of one’s motivation to change than the RTCQ and DBID (Fischoff & Quadrel, 1991). Finally, the open-ended format of the decisional balance worksheet lends itself to potential qualitative as well as quantitative representations of motivation to change.
Limitations
This study comprised a nonrandom sample of at-risk college drinkers who had participated in a larger intervention trial. Considering the potential confounding effects of the nonrandom selection and exposure to brief interventions, it is necessary to replicate these results on a larger, randomly selected, nontreatment sample. Furthermore, the relatively homogeneous racial and ethnic composition of the current sample raises questions as to the external validity of the current findings. This sample consisted of predominantly White, non-Hispanic students; thus, further study of the DBP and its ability to predict drinking outcomes in more diverse samples is necessary to ensure its generalizability to other populations. We also recognize that the DBP focuses on numbers of items generated rather than their content. Although potential information may be gained by considering item content as well, the DBP has the advantage of rapid and reliable scoring. Despite these limitations, the current results provide additional support for and expansion on a quantification of a drinking decisional balance originally introduced by Collins and Carey (2005).
Conclusions
This study has provided evidence for the convergent, discriminant, and predictive validity of a new decisional balance measure of motivation to change drinking behavior among at-risk college drinkers. This study adds to the literature because previous conceptualizations of the decisional balance measure were researcher- instead of participant-generated and were limited in their scope (i.e., assessed either pros and cons of drinking or of changing behavior but not both). Furthermore, this measure appears to predict longitudinal drinking better than established, Likert scale measures of readiness to change and decisional balance. Larger scale studies should be conducted to provide additional support for the psychometric integrity, clinical utility, and generalizability of this decisional balance measure.
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Submitted: October 1, 2008 Revised: January 1, 2009 Accepted: February 20, 2009
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Source: Psychology of Addictive Behaviors. Vol. 23. (3), Sep, 2009 pp. 464-471)
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Record: 2- A tutorial on count regression and zero-altered count models for longitudinal substance use data. Atkins, David C.; Baldwin, Scott A.; Zheng, Cheng; Gallop, Robert J.; Neighbors, Clayton; Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013 pp. 166-177. Publisher: American Psychological Association; [Journal Article] Abstract: [Correction Notice: An Erratum for this article was reported in Vol 27(2) of Psychology of Addictive Behaviors (see record 2013-21666-002). The URL for the supplemental material was incorrect throughout the text due to a production error. Supplemental material for this article is available at: http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp. The online version of this article has been corrected.] Critical research questions in the study of addictive behaviors concern how these behaviors change over time: either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data
By: David C. Atkins
Department of Psychiatry and Behavioral Sciences, University of Washington;
Scott A. Baldwin
Department of Psychology, Brigham Young University
Cheng Zheng
Department of Biostatistics, University of Washington
Robert J. Gallop
Applied Statistics Program, West Chester University
Clayton Neighbors
Department of Psychology, University of Houston
Acknowledgement: We thank Eun-Young Mun, Zac Imel, Isaac Rhew, Jennifer Kirk, and an anonymous reviewer for helpful feedback that improved the manuscript in numerous ways. The example data included in the article were collected via the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grants AA016099 and AA014576 (Clayton Neighbors, PI), and David C. Atkins' time was supported in part by NIAAA Grant AA019511 (Eun-Young Mun, PI).
What is the impact of personalized normative feedback on drinking related problems in college students over time? How does weekend versus weekday drinking vary by gender and fraternity/sorority status when assessed on a daily basis? Many questions about alcohol and substance use focus on change across time, and the methods used to analyze these questions need to account for the longitudinal nature of the data. Generalized linear mixed models (GLMMs; Gelman & Hill, 2007; Hedeker & Gibbons, 2006; also called hierarchical [or multilevel] generalized linear modeling, Raudenbush & Bryk, 2002; Snijders & Bosker, 1999) are increasingly common analytic approaches for longitudinal data, given their flexible handling of unbalanced repeated measures (i.e., individual participants may have unique numbers and timings of assessments) and the widespread availability of software for estimating such models. Moreover, GLMMs are appropriate for continuous as well as discrete outcomes.
However, the distributions of alcohol and substance abuse outcomes have characteristic shapes: They are often positively skewed and bounded by zero. Moreover, there can be a large stack of data points at zero, indicating individuals and/or occasions without drinking, use, or related problems. These distributions reflect that alcohol and substance use outcomes are often count data, representing a total number of something, be it drinks, days using, or number of problems. Except in special circumstances (e.g., specially selected samples with high drinking or drug use), statistical models that assume normally distributed residuals will provide poor fit to such data and will lead to incorrect confidence intervals and p values. Instead, count regression approaches such as Poisson or negative binomial regression or zero-altered count models (e.g., zero-inflated or hurdle models) are much more appropriate for these types of data (Atkins & Gallop, 2007; Coxe, West, & Aiken, 2009; Hilbe, 2011; Neal & Simons, 2007; Simons, Neal, & Gaher, 2006).
In the past, addictions researchers have often ignored (or not been aware of) violations of distributional assumptions or have attempted to deal with them in nonoptimal ways. Count regression models are beginning to be applied to addictions data (e.g., Gaher & Simons, 2007; Lewis et al., 2010), but accessible resources on how to apply these models to longitudinal data are scarce. The present article provides a tutorial in analytic methods for count data from longitudinal studies, focusing on extensions to GLMMs for count outcomes. We use two examples from our research to illustrate the need for, and application of, longitudinal count models. Data and computer code to run the analyses in R, Mplus, SAS, Stata, and SPSS are available on a supplementary website (http://depts.washington.edu/cshrb/newweb/statstutorials.html), though note that not all software can run all models that are covered here at present time. The outline of the article is as follows: Introduction to example data and research questions, brief overview of count regression models, GLMMs for count regression models, analyses and interpretation of example data, and discussion of software and practical issues in using these methods. In addition, there is a technical appendix containing important, but more advanced, material (see online supplemental material; http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp). We assume that readers have a basic familiarity with linear mixed models (i.e., hierarchical linear or multilevel models assuming normally distributed errors) and count regression models, though both are introduced briefly here and introductory resources are highlighted throughout.
Motivating ExamplesThe first example dataset is drawn from an intervention study aimed at reducing problematic drinking in college students (Neighbors et al., 2010). The current article focuses on gender differences across 2 years in alcohol-related problems, as measured by the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989). The dataset includes 3,616 repeated measures across five time points from 818 individuals. The second dataset involves intensive, daily assessment of drinking. The data come from a larger intervention study of event-specific prevention (i.e., drinking related interventions for 21st birthdays and spring break), but the current data are observational. Specifically, these data record the number of drinks for each day over approximately the last 30 days for 980 individuals (23,992 total person-days), as measured by the timeline follow-back interview (TLFB). These data came from a survey study of 21st birthday drinking and consequently include some extreme drinking events relative to a random sample of student's drinking (Neighbors et al., 2011). Analyses focus on drinking differences by gender, Greek status (i.e., fraternity/sorority membership), and weekend (Thurs–Sat) versus weekday (Sun–Wed). (Note that Thursday was included as part of the “weekend” given that drinking on Thursday was more similar to Friday and Saturday drinking than other days of the week.)
Count Regression ModelsCount variables are often positively skewed and often include many observations at zero. The top row of Figure 1 displays (unconditional) distributions of drinking (TLFB) and alcohol problems (RAPI), which are strongly skewed with a mode of zero. The bottom row of Figure 1 shows histograms of residuals from regressing the RAPI on gender and time assuming normally distributed residuals (i.e., ordinary least squares [OLS] regression): on the left without any transformation and on the right with a log transformation of the RAPI. These plots show that skewed count regression outcomes will rarely meet the distributional assumptions of OLS regression or linear mixed models. Moreover, count outcomes will also typically violate the equal variances (i.e., homoskedasticity) assumption of linear models as count outcomes have a direct relationship between their mean and variance, where higher levels of the outcome have greater variance. Although transforming the outcome is a commonly suggested strategy for skewed data, a stack of zeroes will not be smoothed out by a transformation. Moreover, focusing on the TLFB data, the distribution shown in Figure 1 is suggestive of two different types of associations or research questions: (a) what is related to no drinking versus any drinking (i.e., zero vs. nonzero), and (b) what is related to the amount of drinking when there is drinking? Although the zero/nonzero aspects of the RAPI data are not quite as notable, a similar set of questions could be asked of the RAPI data. As will be described later, zero-inflated and hurdle models have submodels that focus on these two questions. Prior to introducing count regression models, we consider the qualities of the example data that are related to the questions just posed.
Figure 1. Plots of frequency counts of daily drinks from timeline follow-back (TLFB; upper left) and Rutgers Alcohol Problems Index (RAPI; upper right). Residuals from fitting an ordinary least squares regression to the RAPI or log-transformed RAPI are in lower left and lower right, respectively.
Consider how the proportion of individuals drinking versus not drinking and number of drinks on drinking days are related to covariates in the TLFB data. Figure 2 presents means and 95% confidence intervals (CIs) for number of drinks on drinking days (top half of graph) and proportion of days drinking (bottom half of graph) by weekday versus weekend, Greek status, and gender.
Figure 2. Means and 95% confidence intervals for drinking on drinking days (top row) and proportion of individuals drinking (bottom row). Means are stratified by weekend versus weekday, male versus female, and fraternity/sorority member or not.
There are strong differences in the proportion of people drinking for weekend versus weekday, with less prominent differences for number of drinks when drinking. Moreover, from this descriptive view, the number of drinks is highly related to fraternity/sorority status, whereas proportion of people drinking does not appear as strongly related to Greek status.
Figure 3 shows a similar set of plots for the RAPI by gender across time. As with the TLFB drinking data, patterns of association appear different across the two aspects of the outcome. Over the five assessments (and following intervention for most participants), the proportion of individuals with any alcohol problems is dropping, with a greater proportion of men consistently reporting more alcohol problems (right panel). When examining the number of alcohol problems (given some problems), we see evidence for divergence between the sexes, with women showing slight decreases in number of alcohol problems, whereas men appear to show slight increases, though with notable variability as seen in the confidence intervals (left panel). Thus, these graphs serve to highlight that different associations can occur with outcomes with notable zeroes (i.e., whether there is any drinking [zero vs. not zero] and amount of drinking when any drinking).
Figure 3. Means and 95% confidence intervals for number of problems when there are any problems (left) and proportion of individuals reporting any drinking-related problems (right). Means are stratified by assessment period and gender.
Before moving on to discuss GLMMs for count outcomes, we briefly introduce count regression models (more thorough introductions can be found in articles by Atkins & Gallop, 2007, and Coxe et al., 2009, and the book by Hilbe, 2011). The basic count regression model is Poisson regression, which is one of the generalized linear models (McCullagh & Nelder, 1989). There are two critical differences between OLS regression and Poisson regression. First, the outcome (conditional on covariates) is assumed to be distributed as a Poisson random variable as opposed to a Normal random variable. The top row of Figure 4 depicts three different Poisson distributions, with varying means (denoted by the Greek letter mu). This figure underscores that the Poisson distribution is a discrete distribution for non-negative integers, the exact qualities of count variables (i.e., a count variable cannot be negative or fractional). Second, in Poisson regression the linear predictor of the regression model (i.e., the right-hand side of the regression equation) is connected to the outcome via a natural logarithm link function. Although this is not identical to transforming the outcome, it does mean that the regression coefficients from a Poisson model are on a log scale. Similar to logistic regression, raw coefficients are typically raised to the base e (i.e., exponentiated, the antilog function) and interpreted as rate ratios. Like odds ratios (ORs), rate ratios are inversely proportional around one (i.e., a rate ratio of 3 is equal in strength but opposite in direction to a rate ratio of 1/3). Rate ratio interpretation will be described in greater detail later, in the applications section.
Figure 4. Plots of frequency counts of data simulated from Poisson distributions with three different means (top row). The bottom row contains frequency counts of data simulated from negative binomial distributions with the same mean but varying dispersion.
The Poisson distribution (and regression) has an Achilles' heel of sorts in that it has the property that the mean equals the variance. In real data the variance often far exceeds the mean, and we would say that the data are overdispersed relative to the Poisson distribution. Using descriptive statistics, dispersion is typically defined by the ratio of the variance to the mean. Thus, a Poisson distribution assumes a dispersion parameter of 1 (also called equidispersion). A dispersion parameter of 3 would indicate a variance value of three times the mean, which would (descriptively) indicate overdispersion. Both of our example datasets show evidence of overdispersion simply based on descriptive statistics (TLFB: M = 1.2, Var = 9.3; RAPI: M = 6.3, Var = 82.9), though overdispersion can also be influenced by the longitudinal nature of these data and may be accounted for by covariates included in the model. Note that this description is meant to convey the intuitive ideas underlying overdispersion, but formal tests can (and should) be used in regression modeling of count data (see, e.g., Hilbe, 2011).
The negative binomial model extends the Poisson model by allowing the mean and variance to be different. The lower row of Figure 4 presents three different negative binomial distributions. Note that each of these distributions has the same mean, but the dispersion varies, highlighting the primary difference between the Poisson and negative binomial distributions. The Poisson regression model is a special case of the negative binomial model, and when the mean equals the variance, the two will yield identical results. However, when the variance exceeds the mean, the negative binomial is more appropriate, and its standard errors will be reliably larger than those from the Poisson, reflecting the additional variance in the outcome. In practice, the Poisson model is rarely a good fitting model for exactly this reason, and selecting a Poisson model when the data are overdispersed will yield overly liberal statistical tests (i.e., p values will appear significant when they are not in reality, using a more appropriate model).
As seen in Figure 4, the negative binomial regression model can fit highly skewed data, including data with a relatively large number of zeroes. However, when there is a clear stack of zeroes in the data and especially when the nonzero distribution is not a smooth extension from the zeroes, alternative models may be appropriate. Two closely related models explicitly handle count data with zeroes above and beyond what would be predicted by a negative binomial model: zero-inflated models and hurdle models (see, e.g., Hilbe, 2011; Zeileis, Kleiber, & Jackman, 2008). Both models include a logistic regression for the zeroes in the data and a count regression (either Poisson or negative binomial) for the counts. However, zero-inflated and hurdle models take different approaches to dividing the data around zero. The predicted zeroes in a zero-inflated model come from both the count distribution and an extra mass of zeroes and are a type of mixture model in which the distribution at zero arises from two sources (i.e., count and logistic submodels). Thus, the logistic regression model in a zero-inflated model is for “excess zeroes,” over and above what would be predicted by the count distribution. Hurdle models, on the other hand, model all the zeroes in the logistic regression, and nonzero counts are modeled by a truncated count regression (i.e., truncated because it does not include zero). Hurdle models are related to the more general class of models, often called “two-part models,” in which a logistic model for zero versus nonzero is combined with a model for nonzero values. In the current article we consider only Poisson and negative binomial for nonzero models, but this does not have to be the case (see, e.g., health care costs as in Buntin & Zaslavsky, 2004). In many instances zero-inflated models and hurdle models will yield similar results; however, hurdle models are more straightforward to interpret as all zeroes are handled in one portion of the model, and computationally, hurdle models are somewhat easier to fit as the two parts of the model can be fit independently of one another.
All of the count regression models introduced thus far assume independent observations. This assumption is violated in longitudinal data because repeated observations on the same individual will be correlated. To accommodate this nonindependence of observations, mixed model extensions to count regressions can be used.
Generalized Linear Mixed Models for Count OutcomesThe top half of Figure 5 displays change across time in the RAPI for eight randomly selected individuals. As seen in that plot individuals started the study with a wide range of alcohol problems, and some individuals made notable changes in alcohol problems, whereas others did not. To illustrate the variability in intercepts (i.e., initial RAPI) and slopes (i.e., change over time in RAPI), Poisson regressions were fit to each individual's data (i.e., RAPI was regressed on time for each individual separately, using a Poisson regression; see Singer & Willett, 2003, for general discussion of this approach). The distributions of intercepts and slopes are plotted in the bottom of Figure 5. The means of these distributions are descriptive estimates of average intercepts (M = 8.2) and slopes (M = 0.99), and the distributions themselves highlight the variability across individuals in intercepts and slopes.
Figure 5. RAPI scores plotted over time for eight randomly selected individuals (top row). Distribution of intercepts (bottom left) and slopes (bottom right) from fitting separate Poisson regressions to each individual's data.
GLMMs for longitudinal data extend this logic of individual growth curves by including subject-specific variability in one or more components of the model (e.g., intercepts, slopes) via additional variance terms that describe a distribution of regression coefficients across individuals within the study. GLMMs have been referred to as random coefficient models for precisely this reason. Using the systems of equations or hierarchical format with explicit Level 1 and Level 2 models, a Poisson GLMM for the RAPI data might be
which is identical to the composite form of the model by substituting the Level 2 equations into their Level 1 counterparts:
where t indexes time, i indexes individuals, and the linear predictor (i.e., right-hand side of equation) is connected to the mean of the outcome via a natural logarithm link function. Male is a dummy-variable for gender (female = 0, male = 1), and Time measures time in months since the start of the study. The two variance terms (u0i, u1i) describe the deviations of each participant's intercept and slope around the overall trajectory for the sample, defined by the fixed effects (i.e., b0+ b1Male + b2Time). As is common with error terms, these variances are assumed (multivariate) normally distributed. Finally, the model assumes that the conditional distribution of the outcome given fixed and random effects is Poisson distributed. It is important to note that both the fixed and random effects are connected to the outcome via the link function, which has implications for interpretation of the model (see discussion of marginal vs. conditional effects, below).
The results for the model in Equation 1 are presented in Table 1. Focusing first on the fixed effects, the subject-specific rate ratios (RRs) are simply the exponentiated coefficients (e.g., for the intercept, e1.45 = 4.28). The intercept RR provides the estimated alcohol problems at baseline for women (i.e., when all covariates are equal to zero, as in all regression models), conditional on the random effects. The RR of 1.29 for men indicates that their alcohol problems are 29% higher than women on average. Generally, the distance above or below 1 is interpreted as the percentage increase or decrease in the outcome for a one-unit increase in the predictor. Similarly, the 0.97 RR for time implies a 3% reduction in RAPI for each 1-month increase. Let's consider the interpretation of RRs a bit more closely. The predicted regression lines based on the fixed effects are shown in Figure 6, and the specific values are presented in the figure as well. If this were a linear mixed model or OLS regression, the predicted regression lines would be perfectly straight and parallel (i.e., because there is no interaction between Male and Time), but it is clear from the figure that the predicted regression lines for men and women from the Poisson GLMM are curved and are getting closer to one another over time. Poisson models with their log link functions are sometimes called multiplicative models, whereas OLS regression (and linear mixed models) are considered additive. For the latter, the regression coefficients tell us how much to “add” to our prediction for each one-unit increase in a covariate.
GLMM Poisson Results for RAPI Data
Figure 6. Predicted (unit-specific) RAPI scores from Poisson GLMM for men and women over time, with specific values noted in text.
The interpretation of multiplicative models is more complicated. First, we exponentiate the fixed effects in Equation 1b:
where exp is the exponentiating function, raising to the base e. We then reexpress Equation 1c using a property of exponetiated sums:
Equation 1d shows why Poisson models are considered multiplicative: The exponentiated coefficients—the RRs—provide a multiplying factor for each unit change in the covariate. Thus, the RR of 1.29 for men indicates that men's predicted alcohol problems are 1.29 times women's predicted alcohol problems at each level of time, which is identical to saying that it is 29% higher (e.g., at time = 0, 4.28 × 1.29 = 5.52, and at time = 24 months, 2.03 × 1.29 = 2.62). For every 6 months, the model predicts alcohol problems are falling by e−0.03∗6 = 0.83 (i.e., an RR for each 6-month change in time). We can similarly confirm that each 6-month change in predicted alcohol problems is 17% less (i.e., multiply by 0.83) than the preceding predicted value. Thus, for OLS regression and linear mixed models, coefficients describe the amount added to predictions with each one-unit covariate change, whereas Poisson and Poisson GLMM coefficients describe the amount multiplied.
One critical feature of these coefficients that has not been widely noted in the applied literature is that these coefficients (and their interpretation) are conditional on specific values of the random-effects distribution. In linear mixed models, fixed-effect coefficients can be interpreted as “averaging over” the random effects (sometimes called marginal coefficients), but this is not true in GLMM with nonidentity link functions (see, e.g., Heagerty & Zeger, 2000 and Raudenbush & Bryk, 2002, Chapter 10). This issue of conditional versus marginal coefficients (also called unit-specific vs. population-average) is discussed in the online supplemental material (see http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp), along with additional resources.
There are several considerations in judging how well the model fits the data. First, similar to all regression models, we should consider whether we have the correct predictors included in the model and whether any terms have nonlinear associations with the outcome. These decisions should be informed by past research, theory, and thorough descriptive statistics and graphs. In fact, it is hard to overemphasize the importance of thorough descriptive analyses to help guide decisions in building complicated models, such as those considered here. In addition, similar to many regression models, GLMMs assume that variance terms are normally distributed and homoskedastic, and plots of these assumptions can be found in the accompanying online materials (see eFigure 1 in online supplementary material at http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp). Second, similar to linear mixed models we should consider which terms in our model should have corresponding random effects; with GLMMs decisions about which random effects to include are usually determined by testing nested models via deviance tests (also called likelihood ratio chi-square tests; Molenberghs & Verbeke, 2005). Based on descriptive statistics and graphs as well as deviance tests for random effects, the initial model for the RAPI was extended to include an interaction between gender and time and an additional per-observation random effect (discussed below). These results are shown under Model 2 in Table 1.
Most (but not all) GLMMs are fit via a procedure called maximum likelihood, and when the iterative fitting procedure stops, a deviance statistic is reported (technically, −2 × log-likelihood, and sometimes reported as −2LL). The difference between deviances from two nested models is distributed as a chi-square random variate and hence can be tested using a chi-square distribution with degrees of freedom equal to the difference in parameters between the models (see Singer & Willett, 2003, for further details). Deviance tests can also be used with count regression GLMMs, with one major caveat. Fitting GLMMs with non-normal outcomes (e.g., binary or count outcomes) is considerably more challenging, and there are a variety of optimization strategies and algorithms. Only certain algorithms have been shown to be accurate enough to allow deviance tests of nested models. A brief overview of algorithms and approaches is presented in the online supplemental material (see http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp).
There are two final, critical aspects of model evaluation for GLMMs for count outcomes: overdispersion and zeroes. As noted earlier, the Poisson GLMM assumes that, conditional on fixed and random effects, the distribution of the outcome is distributed as a Poisson variable, with its property of equidispersion (i.e., mean equal to the variance). The most straightforward way to examine whether data are overdispersed relative to the Poisson is to fit a model that allows for overdispersed data. Logically, we might consider the negative binomial. For technical details that go beyond our focus here, the negative binomial model is challenging (though not impossible) to extend to random effects (see, e.g., Hilbe, 2011, pp. 488–501), though some software packages are beginning to incorporate the negative binomial with random effects (e.g., Mplus, R, SAS). With GLMMs we can extend the Poisson model to include a per-observation error term, which captures overdispersion. This type of model is often called an overdispersed Poisson model, which is functionally similar to a negative binomial model (see Rabe-Hesketh & Skrondal, 2008, pp. 389–390, or Ver Hoef & Boveng, 2007, for further discussion). Moreover, adding the extra error term is very similar to a residual error term, as in models for normally distributed data, and can be assessed via a deviance test. Model 2 in Table 1 fits an overdispersed Poisson GLMM, incorporating this extra error term. A deviance test for assessing the improvement in fit due to the addition of the per-observation error term is highly significant, χ2(1) = 2,032.8, p < .01.
Earlier, we commented on the number of zeroes descriptively in the RAPI data, and a final component with count regression models is to examine how well the model replicates the number of zeroes, and more generally, the distribution of counts in the raw data; that is, what does the model predict the histogram of counts looks like, and specifically, what the zeroes look like? A figure included with the supplementary online material presents the raw distribution of the RAPI along with estimates from model two in Table 1 (see Figure 2 at http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp). Although Model 2 appears quite accurate for counts greater than 2, there is noticeable lack of fit for counts of 0, 1, and 2. Essentially, the Poisson distribution is not flexible enough to fit the steep drop of counts from 0 to 1, with much more moderate change for counts greater than 1. As a result the raw RAPI data has 756 zeroes and 395 ones, whereas the final overdispersed Poisson GLMM predicts 616 zeroes and 546 ones. This is a primary statistical motivation for considering a zero-inflated or hurdle mixed model.
Following our example with the RAPI, a hurdle mixed model would be
where t indexes time, i indexes individuals, l and c index logistic and count portions of the model, and p is the proportion of RAPI scores greater than 0. As noted earlier, zero-inflated and hurdle models have two submodels, one related to the zeroes and a second related to the counts. The key difference between hurdle and zero-inflated models is how they handle zeroes: Hurdle models cleanly divide the models, with all zeroes accounted for in the logistic regression, whereas zero-inflated models treat the zeroes as a mixture (i.e., both submodels in zero-inflated models contribute zeroes). As we saw with the overdispersed Poisson GLMM, the hurdle GLMM presented above adds random effects to the linear predictors, with the major difference now being that there are two linear predictors. The random intercept in the logistic model implies that there is variability across individuals in the likelihood of reporting any problems, whereas the random intercept and slope in the count regression models variability in the intercept and change across time in number of problems when there are problems reported. A random slope for time in the logistic portion would model individual variability over time in proportion of zeroes.
Prior to fitting the hurdle mixed model, let us say a word about when to use zero-inflated or hurdle models. Similar to many model selection decisions, the choice between models should include statistical considerations, theoretical considerations, and parsimony. As noted earlier the overdispersed Poisson GLMM of the RAPI data underpredicted zeroes (756 in the raw data vs. 616 predicted by the model), and with the TLFB data, the same type of model led to a highly non-normal distribution for the per-observation error term, based on a histogram and quantile-quantile plot of this error term. Thus, in both of these cases a zero-inflated or hurdle mixed model might be preferred on statistical grounds. However, the statistical motivations just noted do not automatically mean that covariates have important and distinct relationships across the logistic and count portions of a hurdle (or zero-inflated) model. That is, sometimes a hurdle or zero-inflated mixed model will fit the data better, but the conclusions are largely the same compared to a Poisson GLMM (typically because the covariates are primarily related to the nonzero counts). In this scenario, parsimony might suggest the simpler Poisson GLMM is adequate as it yields similar conclusions, though we strongly suggest fitting the more complex model to test whether the simpler model is adequate. Finally, theory may make clear predictions about the proportion of days with any drinking or the amount drunk on days with drinking. These would clearly prefer hurdle or zero-inflated models.
Results from the hurdle mixed model shown in Equations 2a and 2b are presented in Table 2. In examining Table 2, we find that these results map on to the earlier descriptive graphs quite closely. In particular, the count portion of the model shows that men tend to have more alcohol problems when there are problems at the start of the study (RR = 1.21). Because of the coding of the gender dummy-variable, the main effect of time describes the change in women's alcohol problems across time. The RR of 0.98 implies that, conditional on the random effects, women's RAPI (i.e., alcohol problems when there are problems) is dropping 2% with each successive assessment, whereas the interaction between gender and time implies that men's alcohol problems (when there are problems) are barely changing at all, which is confirmed by estimating the men's simple slope for time, RR = 0.99, 95% CI [0.98, 1.00].
Over-Dispersed Poisson Hurdle Mixed Model Results for RAPI Data
The logistic portion of the model describes the proportion of the sample reporting some alcohol problems. (Note that software for zero-inflated and hurdle models are not always consistent as to whether the logistic portion predicts zeroes as opposed to nonzeroes. Here it is predicting nonzeroes.) The ORs in Table 2 show that there are no differences between the sexes in the proportion of individuals reporting alcohol problems at the start of the study, OR = 0.83, 95% CI [0.59, 1.18]. For women, the odds of reporting any alcohol problems decreases by 3% per month, OR = 0.97, 95% CI [0.95, 0.99], conditional on the random effects. For men, their rate of change is somewhat slower, OR = 1.02, 95% CI [0.99, 1.04].
TLFB ApplicationIn turning to the TLFB data, the earlier plots showed that there was a very large stack of zeroes and that covariate effects (i.e., gender, weekend, and fraternity/sorority status) might vary across submodels defined by likelihood of any drinking (i.e., zero vs. not zero) and amount of drinking when there is drinking. These considerations would point toward a zero-inflated or hurdle model; moreover, preliminary models using an overdispersed Poisson GLMM showed that there was a very poor fit between predicted and observed count distribution, and the per-observation random-effect distribution was not close to being normally distributed. Given these considerations, a hurdle overdispersed Poisson GLMM was fit to the TLFB data, fitting main and two-way interaction effects in both submodels via three dummy variables: Male (0 = Women, 1 = Men), Weekend (0 = Sun/Wed, 1 = Thurs/Sat), and FratSor (0 = Not in fraternity/sorority, 1 = Fraternity/Sorority). A random intercept and random slope for weekend were included in both the logistic and truncated count submodels, as well as the per-observation random intercept in the truncated count submodel. Conditional (or unit-specific) fixed-effects are shown in Table 3. In the logistic portion of the model, there is a single, significant effect for weekend: Many more students drink, regardless the quantity, on the weekends as compared to the weekdays, OR = 3.06, 95% CI [2.60, 3.54]. In the count submodel, all three two-way interactions are significant. One approach to interpreting interactions is to use “simple slopes” (see, e.g., Jaccard, 2001). However, in the present case, the model is estimating mean drinking on drinking days for eight specific groups—for each combination of gender, weekend, and fraternity/sorority status. To help interpret the findings of the count submodel, we estimated marginal (or population-average), predicted means for each of these eight groups, which are shown in Figure 7. The figure reveals several interesting findings. Not surprisingly, fraternity and sorority members drink more on drinking days than students who are not in fraternities and sororities, and fraternity and sorority members' drinking is similar on weekdays versus weekend—when they are drinking (i.e., the count submodel in a hurdle is only for nonzeroes). However, contrasts of weekend versus weekday drinking for men and women who are not in fraternities and sororities shows that they do drink more on weekends, women: RR = 1.07, 95% CI [0.99, 1.14], p = .06; men: RR = 1.20, 95% CI [1.12, 1.28], p < .01. Thus, the hurdle mixed model provides a more nuanced view of drinking differences across these groups, parsing out drinking versus not drinking and mean drinking on drinking days.
Over-Dispersed Poisson Hurdle Mixed Model Results for TLFB Data
Figure 7. Predicted (marginal) drinks for each unique combination of gender, weekend (WE) versus. weekday (WD), and fraternity/sorority status from count submodel of overdispersed Poisson hurdle mixed model.
Software for GLMMsStatistical software packages are steadily expanding their coverage of GLMMs and the models covered in the current paper. Table 4 presents an overview of SPSS, Stata, R, SAS, and Mplus, describing which of the models covered in the present article are possible to fit. As seen there, the basic Poisson GLMM is implemented in all software, whereas negative binomial GLMM and zero-altered models are less widely available. The online materials provide computer code for fitting some example models in each of the packages mentioned, and the online supplementary material covers additional details related to fitting GLMM, which is both more technical but still practically important (see http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp). Not all software packages were able to fit all models described in the present article (e.g., SAS NLMIXED can fit zero-inflated mixed models but could not fit all the variance components describe in the TLFB example). Over the coming years, there will likely be greater software availability for fitting the types of models covered here as well as more robust fitting algorithms.
Comparison of Software for Fitting Generalized Linear Mixed Models (GLMM)
Summary
The present article discussed extensions to count regression and zero-altered count regression models to longitudinal data based on GLMM. We hope that this presentation, along with the appendix and available data and code, helps addiction researchers to learn and appropriately apply these models. Given the research designs and data that addiction researchers often collect, the models covered here are often the most appropriate analytic tools. At the same time, like other statistical models that are finding their way to the applied research literature (e.g., growth mixture models; Muthén & Muthén, 2000), multilevel count models are complex. The types of assumptions and general considerations in model fitting grow exponentially from linear models to generalized linear models to linear mixed models to generalized linear mixed models. We encourage researchers to explore and use these tools as their hypotheses and data merit, though also note that they require a serious commitment of time and study to be used appropriately.
Footnotes 1 The six graphs in Figure 4 were created by simulating 1,000 random draws from the specified Poisson or negative binomial distributions. Thus, these are similar to frequency histograms, except there is no binning of multiple values.
2 There are alternatives to the negative binomial distribution in terms of dealing with overdispersed count data, including quasi-Poisson models, Poisson-normal models, and robust standard errors. Details on these models can be found in Hilbe (2011).
3 Note that the distribution of slopes appears somewhat odd, with spikes at extremely high or low values. This reflects that some individuals have incomplete data, and hence, slopes fit to two or three points of data can yield very extreme values. The subject-specific predictions from GLMMs pool information about the sample and the individual's data, leading to more sensible fits for individuals (see, e.g., discussion of empirical Bayes estimates in Raudenbush & Bryk, 2002).
4 The notation in Equation 1 is slightly nonstandard. In the statistical literature it is common to see GLMMs described in terms such as: η = XB + Zb, with g(μ) = η, and g( ) = log. Translating into words this means that η is the linear predictor, or right-hand side of the model, including both fixed and random effects (i.e., XB and Zb, respectively). The linear predictor is connected to the mean of the outcome (i.e., μ) via some function. With the Poisson model, the link function is the log.
5 Estimating the predicted number of zeroes based on a GLMM takes us into somewhat more technical material. The Poisson GLMM can be represented by Pois(XB + Zb), where X is the design matrix of the fixed-effects; B is a vector of fixed-effects coefficients; Z is the design matrix of the random effects; and b is a vector of random-effects variances. The key thing to realize is that the formula within the parentheses defines the fitted values of the model. Thus, we can read this equation as follows: The data are distributed as a Poisson random variable with a mean structure defined by the fixed and random effects of the GLMM. Similar to the discussion of conditional effects, the random effects create additional challenges in estimating the model-based distribution of counts. The accompanying R code shows one method for simulating from the fitted model to estimate the predicted distribution of counts.
6 We note in passing that the hurdle mixed models reported in this article were fit using the MCMCglmm package (Hadfield, 2010) in R, which uses a Bayesian approach to model estimation. For many practical problems maximum likelihood estimation and Bayesian MCMC estimation will yield similar if not identical results, though there are basic differences in both inferential philosophy as well as estimation. A brief discussion is provided in the technical appendix of the online material (see http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp). In this particular instance, the choice is quite pragmatic as MCMCglmm provides the most flexible option for fitting these types of models in R.
7 To estimate marginal (or population-average) estimates from a GLMM, it is necessary to include both fixed and random effects. The online supplementary material provides details and the example of how the present estimates were created (see http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp).
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Submitted: November 22, 2011 Revised: June 15, 2012 Accepted: June 18, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 166-177)
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Digital Object Identifier: 10.1037/a0029508
Record: 3- 'A tutorial on count regression and zero-altered count models for longitudinal substance use data': Correction to Atkins et al. (2012). Atkins, David C.; Baldwin, Scott A.; Zheng, Cheng; Gallop, Robert J.; Neighbors, Clayton; Psychology of Addictive Behaviors, Vol 27(2), Jun, 2013 Special Issue: Neuroimaging Mechanisms of Change in Psychotherapy for Addictive Behaviors. pp. 379. Publisher: American Psychological Association; [Erratum/Correction] Abstract: Reports an error in 'A tutorial on count regression and zero-altered count models for longitudinal substance use data' by David C. Atkins, Scott A. Baldwin, Cheng Zheng, Robert J. Gallop and Clayton Neighbors (Psychology of Addictive Behaviors, 2013[Mar], Vol 27[1], 166-177). The URL for the supplemental material was incorrect throughout the text due to a production error. Supplemental material for this article is available at: http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2012-22398-001.) Critical research questions in the study of addictive behaviors concern how these behaviors change over time: either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Correction to Atkins et al. (2012)
In the article “A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data” by David C. Atkins, Scott A. Baldwin, Cheng Zheng, Robert J. Gallop, and Clayton Neighbors (Psychology of Addictive Behaviors, Vol. 27, No. 1, pp. 166–177. doi:10.1037/a0029508), the URL for the supplemental material was incorrect throughout the text due to a production error.
Supplemental material for this article is available at: http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029508.supp. The online version of this article has been corrected.
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Source: Psychology of Addictive Behaviors. Vol. 27. (2), Jun, 2013 pp. 379)
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Record: 4- Adolescent change language within a brief motivational intervention and substance use outcomes. Baer, John S.; Beadnell, Blair; Garrett, Sharon B.; Hartzler, Bryan; Wells, Elizabeth A.; Peterson, Peggy L.; Psychology of Addictive Behaviors, Vol 22(4), Dec, 2008 pp. 570-575. Publisher: American Psychological Association; [Journal Article] Abstract: Homeless adolescents who used alcohol or illicit substances but were not seeking treatment (n = 54) were recorded during brief motivational interventions. Adolescent language during sessions was coded on the basis of motivational interviewing concepts (global ratings of engagement and affect, counts of commitment to change, statements about reasons for change, and statements about desire or ability to change), and ratings were tested as predictors of rates of substance use over time. Results indicate that statements about desire or ability against change, although infrequent (M = 0.61 per 5 min), were strongly and negatively predictive of changes in substance use rates (days of abstinence over the prior month) at both 1- and 3-month postbaseline assessment (ps < .001). Statements about reasons for change were associated with greater reductions in days of substance use at 1-month assessment (p < .05). Commitment language was not associated with outcomes. Results suggest that specific aspects of adolescent speech in brief interventions may be important in the prediction of change in substance use. These relationships should be examined within larger samples and other clinical contexts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Adolescent Change Language Within a Brief Motivational Intervention and Substance Use Outcomes
By: John S. Baer
Alcohol and Drug Abuse Institute, University of Washington;
Department of Psychology, University of Washington;
Blair Beadnell
Alcohol and Drug Abuse Institute, University of Washington;
School of Social Work, University of Washington
Sharon B. Garrett
Alcohol and Drug Abuse Institute, University of Washington
Bryan Hartzler
Alcohol and Drug Abuse Institute, University of Washington
Elizabeth A. Wells
Alcohol and Drug Abuse Institute, University of Washington;
School of Social Work, University of Washington
Peggy L. Peterson
Alcohol and Drug Abuse Institute, University of Washington;
Department of Psychology, University of Washington
Acknowledgement: The research reported herein was supported by National Institute on Drug Abuse Grant R01 DA15751. We gratefully acknowledge the contributions of research assistants Sarah Bowen and Dana Rhule; counselors Jennifer Mullane, Melissa Phares, and Maija Ryan; data coders Avry Todd and Kate Hallman; the staff at New Horizons Ministries; and Dan Kivlahan for comments on a draft of this article.
Motivational interviewing (MI; Miller & Rollnick, 2002) is a popular, empirically based counseling method for a range of health-related problems. Defined as a “directive, client-centered counseling style for eliciting behaviour change by helping clients explore and resolve ambivalence” (Rollnick & Miller, 1995, p. 326), MI emphasizes formation of collaborative therapeutic relationships with clients through which client language about change may be strategically elicited and reinforced. Change language is defined as client expressions of problems with the current state, benefits of change, and hope and optimism about future change (Miller & Rollnick, 2002). In articulating a theory for MI, Hettema, Steele, and Miller (2005) stated that (a) the practice of MI should elicit increased levels of change and decreased levels of resistance from clients, (b) the extent to which clients verbalize arguments against change (resistance) during MI will be inversely related to the degree of subsequent behavior change, and (c) the extent to which clients verbalize change talk (arguments for change) during MI will be directly related to the degree of subsequent behavior change.
MI should be considered among therapeutic approaches based in the “ordinary language” of clients, in that therapeutic discourse is understood on the basis of common interpretations of what clients say about thoughts, feelings, and behavior (Moyers et al., 2007). The empirical relationship between client language and outcomes thus assumes a central role in the suggested impact of MI; yet evidence for this hypothesized relationship is limited. With respect to verbalizations contrary to change, Miller, Benefield, and Tonigan (1993) reported that independent ratings of client interrupting, arguing, off-task responses, and other negative responses within a brief intervention about alcohol use were strongly predictive of poorer 1-year drinking outcomes. Recently, Moyers et al. (2007) coded client language of participants randomly selected from each of three Project MATCH therapy conditions. Rates of both positive and counter–change language in the first clinical session across treatments predicted follow-up drinking outcomes.
Further evidence of a link between positive client change language and clinical outcomes in MI sessions was reported by Amrhein, Miller, Yahne, Palmer, and Fulcher (2003) in sessions with illicit drug users as they presented for treatment. Client language was subjected to a linguistic analysis that subdivided change language into elements thought to vary with respect to the nature and degree of commitment to change (commitment, desire, ability, need, readiness, and reasons). Clients who reduced their substance use during and after treatment made more frequent commitment to change statements during the evaluation of a change plan at the end of the interview compared with those who intermittently used substances over time or who never improved. Other aspects of change language were predictive of commitment language.
This small, emerging evidence base provides some support for the notion that client change language can prospectively predict clinical outcomes. These studies, however, have been completed with adults who were seeking treatment (or a free checkup; Miller et al., 1993). Yet MI has been adopted in numerous clinical and nonclinical contexts (Miller & Rollnick, 2002). One common focus for brief interventions in opportunistic contexts is with young people, who seldom seek services on the basis of their own concerns for health and safety (Baer & Peterson, 2002; Monti, Colby, & O'Leary, 2001). Despite this interest, we are unaware of studies that describe the target of MI sessions, describe youth verbalizations about behavior change, or examine whether these relate to current or future behavior. Such information would seem important given that aspects of adult language are still developing in adolescents (Nippold, 2000; O'Kearney & Dadds, 2005). Hence, adolescents receiving feedback about drug use risks may talk about change very differently than adults seeking treatment.
The current analyses sought to extend prior studies of client language within MI sessions by examining the language of homeless youths who use alcohol and illicit substances and were recruited for a risk reduction program (Baer, Garrett, Beadnell, Wells, & Peterson, 2007) that utilized a brief motivational intervention (BMI). We tested whether global ratings of client behavior in MI sessions, and behavioral ratings of positive and counter–change language and commitment talk, would predict changes in substance use from baseline to 1- and 3-month follow-ups.
Method Design and Procedures
All procedures were approved by the University of Washington Institutional Review Board. A sample of youths (ages 13–19 years) was recruited from a nonprofit, faith-based drop-in center (Baer et al., 2007) by study counselors. Inclusion criteria included lack of stable housing and report of substance use in the prior 30 days. The 127 youths were randomly assigned to receive four sessions of a BMI (n = 75) or drop-in center treatment-as-usual (n = 52), which included no specific clinical intervention, although case management and other services were available. Only youths receiving BMI are included in current analyses. As MI was not found to be superior to treatment-as-usual in this study (see Baer et al., 2007), and treatment-as-usual received no specific treatment sessions, mediating tests of the function of client change for MI were not possible. Follow-up assessments were conducted via appointments and intercept at the drop-in center and on the streets both 1 month and 3 months postbaseline. Following baseline interviews, youths assigned to BMI stayed for the first of four BMI sessions, with later BMI sessions scheduled in the following 4 weeks. Baseline interviews and the BMI sessions were conducted by a master's-level clinician, with follow-up interviews conducted by alternative project staff. As described elsewhere (Baer et al., 2007), participants received cash for completing study assessments and vouchers redeemable at local retail stores for completed BMI sessions. With consent of participants, baseline interviews and BMI sessions were audiotaped.
Sample description and retention
In the original study attrition was not associated with experimental condition, baseline demographic, or substance use rates. Participants identifying themselves as minority racial group members were retained at slightly higher rates compared with those self-identified as nonminority. Of 75 youths assigned to BMI, 21 were removed from analyses due to extensive missing data or concerns about validity of reported substance use. These included youths who (a) were incarcerated in the 30 days prior to baseline assessment (n = 3) or a follow-up assessment (n = 4), (b) received strongly negative interviewer ratings regarding consistency of their responses (n = 2), (c) refused to be audiotaped (n = 6) or were eliminated because of taping malfunction for at least half of the session (n = 4), (d) attended only highly abbreviated sessions (n = 1), and (e) missed both follow-up assessments (n = 1). No differences were seen between those included versus those excluded in relation to gender, age, or racial or ethnic minority (all ns). At baseline the 21 excluded by these criteria reported less substance use in the previous 30 days than the 54 included (days abstinent M = 14.0, SD = 8.8 vs. M = 7.5, SD = 8.9), t(73) = 2.84, p < .01.
The resulting youth sample for current analysis showed a fairly even gender distribution (54% male vs. 46% female) and mean age of 17.9 years (SD = 1.3). Reported ethnicity was 59% Caucasian, 17% multiracial, 7% Native American, 7% African American, 6% Hispanic or Latino, and 4% Asian or Pacific Islander. The sample reported high rates of substance use, with a mean of 7.5 (SD = 8.9) days of abstinence from substances in the month prior to baseline assessment. Alcohol use in the past month was reported by 89.1% of the sample. Marijuana was the most commonly used drug in the past 30 days by youth report (94.4%), followed by “club drugs” (57.1%), methamphetamine (53.5%), hallucinogens (35.9%), cocaine (33.3%), and opiates other than heroin (33.3%).
Intervention description
The BMI followed the model of an extended substance use “checkup” in which information or exercises about patterns and risks of substance use are provided as personal feedback (Miller, Sovereign, & Krege, 1988). Intervention content is described elsewhere (Baer et al., 2007). Interventionists were trained in MI techniques, then supervised via regular-session audiotape review by John S. Baer (a licensed psychologist, member of the Motivational Interviewing Network of Trainers, and an experienced MI trainer). Interventionists were trained to be nonconfrontational in tone and to provide advice about risk reduction only with permission. Initial sessions averaged 17 min (SD = 7), with subsequent sessions averaging 33 min (SD = 11). Of the 54 youths assigned to BMI, 27 attended all four sessions, 9 attended three, 9 attended two, and 9 attended one. Mean intervention time across sessions was 79.8 min (SD = 42.9). Youth ratings of the intervention were overwhelmingly positive (Baer et al., 2007).
Measures
Substance use frequency and severity
Participants were asked to recall substance use in the prior 30 days through a modified Time Line Follow-Back interview (Sobell & Sobell, 1993). Given that individuals used many combinations of substances over the 30-day period, an omnibus measure of “days of abstinence” from alcohol, marijuana, and other drug use (excluding tobacco) was calculated. We used abstinence days for conceptual clarity because days of use may represent days of use of any number of substances, whereas abstinence is clearly defined by no substance use.
Validation of drug use self-report
Urinalysis was used to calculate sensitivity and specificity of self-reported cocaine, amphetamine, opiate, and marijuana use at the 3-month follow-up. With 41 of 54 youths (76%) providing urinalysis, no evidence of systematic underreporting was observed (Baer et al., 2007).
Indices of intervention discourse
Audiotaped BMI sessions were coded by three trained raters. The scoring system utilized components of the Motivational Interviewing Skill Code (Version 1.0; Miller, 2000; Moyers, Martin, Catley, Harris, & Ahluwalia, 2003) and psycholinguistic framework of Amrhein et al. (2003) to characterize adolescent language in BMI sessions. Based on the Motivational Interviewing Skill Code, the scoring system included three global ratings of client qualities on 7-point Likert scales (1 = not at all, 7 = strong): (a) affect, how openly and directly emotions were expressed; (b) cooperation, how much responsibility for completing in-session tasks was shared; and (c) disclosure, how much personal information was revealed. An additional global score, task orientation, was added to reflect how much the adolescent remained focused and engaged during therapeutic tasks.
In line with Amrhein et al. (2003), frequency of youth statements about change, termed change talk (Miller & Rollnick, 2002), was tallied relative to substance-related risk reduction, promotion of health and safety, or service utilization. Three categories were coded to reflect differing types of language and degree of interest in change noted in prior research (Amrhein et al., 2003) and based on qualities of adolescent language observed in this and previous studies. These included (a) commitment, or statements of explicit intention to change (or maintain) behavior (e.g., “I'm not going to use meth again” or “I'm never going to stop smoking pot”); (b) reasons, or statements providing a rationale for (or against) behavior change (e.g., “When I'm high I fight with my girlfriend” or “Using drugs helps me cope with being on the street”); and (c) desire/ability, or statements that indicate desire, willingness, or ability or self-efficacy to change or not to change (e.g., “I'd like to cut back my drinking” or “I can't deal with not using because I know it'll be too painful”). Irrespective of categorization, each instance of change talk was also assigned a positive or negative valence reflecting if the statement was in favor of or opposed to reduced substance use, reduced health risks, or increased service utilization. Coders recorded tallies of each type of change talk across 5-min intervals within each session. To control for varying degrees of talkativeness, session lengths, and attendance, we converted tallies to average rates of change talk per 5 min. Overall rates of each type of change talk were calculated across individual sessions, or across all sessions attended, as the total tally across 5-min intervals divided by number of 5-min intervals.
Training and Reliability of Raters
Three raters received extensive training in recognizing and rating MI elements in therapeutic dialogue and attended weekly supervision meetings where scoring dilemmas were discussed. Interrater reliability was assessed from a subset of sessions that all three rated (n = 15, 8.3%). Intraclass correlations, computed as a two-way random model, were acceptable on the basis of Cicchetti (1994) criteria (above .40), although lowest for commitment language (see Table 1).
Coding Reliability, Means, and Standard Deviation for Youth and Counselor Language, Averaged Across Sessions
ResultsDescriptives for session codes are shown in Table 1. For the 7-point scale, mean global scores for cooperation, affect, and task orientation were between 4 and 5, whereas youth disclosure was just above 5. Youths more often expressed reasons for, reasons against, and desire/ability for change, and less often expressed desire/ability against, commitment for, and commitment against change.
Intercorrelations between global scores, change language, and substance use rates at baseline and follow-up assessments are presented in Table 2. As can be seen, desire/ability comments against change were significantly correlated with substance use rates at follow-up assessments. There also are several modest correlations among global ratings and among rates of use of different forms of change language. Among desire/ability and commitment, respectively, statements for and against change were correlated. This may reflect greater talkativeness or suggest that youths may tend to use one form of change language to express both positive and negative motivation. Partial correlations (controlling for the sum of other talk types) suggest that the former may be true for desire/ability for and against change (r = .35, p < .05 vs. partial r = .23, ns) and the latter true for commitment for and against change (r = .40, p < .01 vs. partial r = .39, p < .01).
Intercorrelations Among Language Codes, Global Ratings, and Substance Use Rates (n = 50–54)
Multiple regression assessed whether session language predicted changes in substance use rates. For each follow-up point, multiple regression predicting substance use abstinence in the prior 30 days was performed in Mplus 5.0 through robust maximum likelihood estimation, for sets of predictors. Three participants were missing assessment at 1 month and 4 at 3 months (all 54 had data for at least one follow-up). To assess prediction of change in substance use rates and control for treatment exposure, we used regressions that included baseline prior 30-day abstinence and the number of feedback sessions attended. Demographic characteristics (gender, race, age) were unrelated to either predictors or outcomes in preliminary analyses and were excluded in regressions to preserve statistical power in this small sample. Because counselors were not associated with substance use outcomes in the original study, they also were excluded in regression analyses. As noted in our prior study (Baer et al., 2007), days of abstinence increased over time for those receiving BMI (not differing from those in the control group). For youths included in current analyses, average days of abstinence over the prior 30 days was 7.5 (SD = 8.9) at baseline, then increased to 11.5 (SD = 10.8) at 1-month follow-up and 11.0 (SD = 10.6) at 3-month follow-up.
Tests of the four youth global scores on change in days of abstinence showed that higher task orientation predicted more days of abstinence at the 3-month but not the 1-month follow-up (standardized β = .44, p < .01). No other effects were statistically significant.
In regressions predicting 1-month abstinence rates from youth verbalizations about change, two of the six youth talk rates had statistically significant effects. Reasons in favor of change predicted greater abstinence, and desire/ability against change predicted less abstinence (standardized β = .23 and −.57, respectively, p < .05 and .001). Only desire/ability against change predicted less abstinence at the 3-month time point (standardized β = −.41, p < .05). Figure 1 illustrates the magnitude of effect based on desire/ability against change. Those who expressed more than the median desire/ability against change reported mean abstinence days at the 1- and 3-month follow-ups of 6.9 and 8.7 (SD = 9.1 and 10.0, respectively), whereas those making less than the median amount reported mean abstinence days of 16.2 and 13.4 (SD = 10.5 and 11.0, respectively).
Figure 1. Days of abstinence depending on desire/ability against change talk (individuals above vs. below the median).
Supplemental analyses addressed possible alternative interpretations of results from regression analyses. Outlier analyses (Cohen, Cohen, West, & Aiken, 2003) in which Cook's distance and leverage scores were computed and outliers were removed showed no substantive changes in these findings. Regressions for 1- and 3-month outcomes including significant predictors from the two original regression analyses (global ratings and rates of change language) suggested that the effect of task orientation on 3-month abstinence did not persist once change language was included. Additional analyses revealed no predictive effects of youth talk in the first BMI session, of changes in rates of language across sessions, or differential effects of change talk rated in the first half versus second half of sessions.
Finally, we explored why reasons for change had a nonsignificant zero-order correlation with 1-month abstinence (r = −.03; see Table 2) but was a significant predictor in multivariate regressions (including those controlling for outlier effects). Review of partial correlations with other variables in the regression model suggested statistical suppression only in relation to desire/ability against change (partial r = .27, p = .065). A 2 × 2 analysis of variance of the relation between these two variables in prediction of change in substance use rates, on the basis of median split of each, suggested an interaction, F(1, 3) = 3.10, p = .09. When desire/ability against change was high, there was a detrimental effect on change in abstinence regardless of whether reasons for change was low or high (mean abstinence change = 1.0 and 0.5, for those with low and high reasons for change, respectively, SD = 5.5 and 7.9); however, when desire/ability against change was low, reasons for change appears to affect abstinence (mean abstinence change = 2.9 and 11.7, for those with low and high reasons for change, respectively, SD = 11.2 and 10.3).
DiscussionOur examination of youth verbalizations during BMI sessions provides support for one of the basic tenets of MI: that client change language is related to subsequent behavior change. Despite myriad psychological and social problems among homeless youths and their general disengagement from broader social systems, and despite that this sample was not seeking treatment, change language in BMI sessions regarding substance use or service utilization significantly and prospectively predicted changes in substance use. Analyses suggest that two aspects of youth language about change are differentially indicative of actual behavioral change: statements about reasons in favor of change and statements counter to the desire or ability to change.
The strongest effect was noted for desire/ability language against change. Such comments were strongly and prospectively associated with substance use reductions at both follow-ups, despite being fairly uncommon (mean frequency of .61 per 5 min). Thus, data suggest that within BMI sessions with adolescents, a few comments about not wanting, needing, or being able to change bode poorly for subsequent reduction in substance use. Such utterances may be a marker of a specific type of resistance in MI sessions; resistance has been associated with poor clinical outcomes among adolescents (Karver, Handelsman, Fields, & Bickman, 2006; Shirk & Karver, 2003). This finding is also consistent with literature on behavioral intentions (Fishbein & Ajzen, 1975; Fishbein, Hennessy, Yzer, & Douglas, 2003; Webb, Baer, Getz, & McKelvey, 1996), despite these observational codes differing from typical measures of attitudes and behavioral intention.
Stated reasons for change also reveal a significant prospective relation with outcomes, albeit smaller than that noted for comments about desire/ability against change and possibly relevant only when statements of desire/ability against change are absent. Prior research (Baer, Peterson, & Wells, 2004) suggests that most homeless youths describe themselves as precontemplative or contemplative about change. We expected that, at best, homeless youths would express ambivalence about change in substance use. Given the brief intervention process where feedback is provided about risks and counselors strategically work to elicit positive reasons for change, we expected that problem recognition expressed through reasons for change might emerge. Compared with other forms of change language, positive reasons for change were the most common form of verbalization (1.32 per 5-min interval). Still, this rate does not suggest that these BMI sessions were filled with discussions about negative aspects of substance use and benefits of reduction and cessation. A relatively few stated reasons for change may be important for future behavior nonetheless, especially if youths are not otherwise negative with respect to their desire or ability to change. Our data do not suggest that the rate of such language changed across or within sessions.
Commitment language, which Amrhein et al. (2003) have argued as most important in the prediction of substance use outcomes, was not found to be associated with change in this study. Amrhein et al.'s study differed from this one in that it took place among adults who were seeking treatment for drug problems, and the brief MI intervention included a discussion of a change plan at the end. In contrast, our protocol with homeless youths varied from one to four sessions and followed a booklet from which adolescents could choose topics for discussion. In this context, commitment language was rare (0.27 per 5 min in favor of change, 0.18 per 5 min against change), perhaps accurately reflecting the fact that youths were not seeking assistance. Yet an intervention that includes exercises that elicit commitment language (such as developing a change plan) might produce positive findings about commitment language. Additionally, commitment language among these youths proved difficult to code; intraclass correlations were lowest for this category. A different rating method for this language category might lead to different results.
It is important to acknowledge that this study tests the relationship between youth verbalizations and subsequent behavior only and does not test a complete causal chain for MI efficacy. Several additional features of the study should cause readers to draw conclusions cautiously. We would be more confident in these relationships if we had a larger sample and more variability in youth substance use. Despite a careful approach to data analysis (including evaluating the influence of few cases or outliers), the inherent overfitting of regression models requires that the relationships we have reported be replicated. The study is also limited by the use of self-reported substance use (although urine testing suggests that reports were not systematically biased). Results are based on those youths who were not recently incarcerated and who allowed audiotaping. Generalization to other adolescent populations, both housed and homeless, requires study. Results also relied on a specific coding scheme that was informed by prior studies yet altered by investigators for this study. Alternative coding schemes, which either combine or split constructs differently, may suggest different patterns and predictive relationships. Although we analyzed only those codes that reached a minimum level of confidence based on Cicchetti's (1994) standards, higher and more consistent interrater reliability would enhance confidence in the observed relationships.
Although not providing a direct mediating test, the relationships reported here should provide support for those who design brief interventions in which counselors are taught to pay careful attention to and elicit change language from adolescent clients. Youth verbalizations about change may provide one method of assessment for likelihood of change, which could lead to tailored interventions. For the clinician, taping, transcribing, and reliably coding verbalizations are potentially unfeasible. If these results are replicated, attention to more parsimonious means of assessing change talk will allow for clinical application.
Our data suggest that negative comments about desire or ability against change, even when observed infrequently, are relatively strongly predictive of poor outcomes. Additionally, even among youths with multiple social and psychological difficulties, positive statements about reasons for change may indicate that risk reduction is likely. Whether and how the process of language development itself moderates this relationship would be a fruitful area for exploration. In addition, it is left for future research to establish that the specific eliciting and reinforcing of such change language (e.g., through MI) can in fact alter the course of substance use and other risk behavior among high-risk youths.
Footnotes 1 To assess beliefs about risks and harms associated with substance use, we used codes for reasons for and reasons against change that included statements referring to other people in addition to self. Analyses were completed with and without such codes without appreciable differences in results.
2 Despite lower coding reliability, scores for commitment language were included in initial predictive analyses. Study results did not differ with subsequent exclusion of commitment language from analyses.
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Submitted: October 5, 2007 Revised: April 4, 2008 Accepted: April 23, 2008
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Source: Psychology of Addictive Behaviors. Vol. 22. (4), Dec, 2008 pp. 570-575)
Accession Number: 2008-17215-013
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Record: 5- Application of item response theory to tests of substance-related associative memory. Shono, Yusuke; Grenard, Jerry L.; Ames, Susan L.; Stacy, Alan W.; Psychology of Addictive Behaviors, Vol 28(3), Sep, 2014 pp. 852-862. Publisher: American Psychological Association; [Journal Article] Abstract: A substance-related word-association test (WAT) is one of the commonly used indirect tests of substance-related implicit associative memory and has been shown to predict substance use. This study applied an item response theory (IRT) modeling approach to evaluate psychometric properties of the alcohol- and marijuana-related WATs and their items among 775 ethnically diverse at-risk adolescents. After examining the IRT assumptions, item fit, and differential item functioning (DIF) across gender and age groups, the original 18 WAT items were reduced to 14 and 15 items in the alcohol- and marijuana-related WAT, respectively. Thereafter, unidimensional one- and two-parameter logistic models (1PL and 2PL models) were fitted to the revised WAT items. The results demonstrated that both alcohol- and marijuana-related WATs have good psychometric properties. These results were discussed in light of the framework of a unified concept of construct validity (Messick, 1975, 1989, 1995). (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Application of Item Response Theory to Tests of Substance-Related Associative Memory
By: Yusuke Shono
School of Community and Global Health, Claremont Graduate University;
Jerry L. Grenard
School of Community and Global Health, Claremont Graduate University
Susan L. Ames
School of Community and Global Health, Claremont Graduate University
Alan W. Stacy
School of Community and Global Health, Claremont Graduate University
Acknowledgement: This research was supported by two grants from the United States Department of Health & Human Services, National Institutes of Health, National Institute on Drug Abuse (DA024659-04 and DA023368-06). We thank Amy Custer for her work on this project.
In the past two decades, implicit memory and cognition approaches have gained substantial popularity in addiction and health behavior research. Focusing on the role of spontaneously activated cognitions on behavior (Stacy & Wiers, 2010; Wiers & Stacy, 2006), researchers have examined automatic/implicit cognitive processes at different levels of analysis, ranging from attention (e.g., Bradley, Field, Mogg, & De Houwer, 2004; Mogg & Bradley, 2002) to memory (e.g., Krank & Goldstein, 2006; Stacy, 1995, 1997) and attitude (e.g., Chassin, Presson, Sherman, Seo, & Macy, 2010; Huijding, de Jong, Wiers, & Verkooijen, 2005; Houben, Havermans, & Wiers, 2010) by using various indirect tests of implicit cognitive processes related to addictive and health behaviors (see Ames et al., 2007; Stacy, Ames, & Grenard, 2006; Stacy & Wiers, 2010, for review). A recent meta-analysis examining the relationship between substance-related implicit cognition and substance use revealed that the substance-related implicit word-association test (WAT) was the best predictor of substance use with the largest effect size (mean r = .38) among other implicit measures (Rooke, Hine, & Thorsteinsson, 2008). Although studies have reported good reliability of WAT (Ames et al., 2007; Preece, 1978), to the best of our knowledge, no comprehensive psychometric evaluations of WAT have been conducted in research on addiction, cognition, or memory. The current study extends previous research by applying a comprehensive item response theory (IRT) framework to understand and improve the psychometric properties of WAT items and estimation of underlying latent traits of alcohol- and marijuana-related associative memory.
WATs in Addiction and Health Behavior ResearchThe WAT is one of the most commonly used indirect memory tests for assessing the retrieval of preexisting substance-related associations in memory (Stacy, 1995, 1997). In substance-related WAT, a series of substance-related cue words or phrases are presented one by one visually or auditorily, and participants are asked to generate the first word or short phrase that comes to mind when they think of the cue. It is assumed that an association of a cue–target pair gets strengthened with repetitive encounters with a substance-related cue (e.g., “feeling good”) and target behavior (e.g., marijuana use). Therefore, those who frequently engage in substance use are more likely than those who do not to spontaneously think of substance-use behavior in response to substance-related cues in WAT.
Accumulated evidence has shown that substance- or risky behavior-related implicit associative memory, measured by WAT, has strong predictive power for substance use, including alcohol (Ames & Stacy, 1998; Kelly, Masterman, & Marlatt, 2005; Stacy, 1997), marijuana (Ames et al., 2007; Ames & Stacy, 1998; Stacy, 1997), and cigarette use (Grenard et al., 2008; Kelly, Haynes, & Marlatt, 2008), as well as risky sexual behavior (Ames, Grenard, & Stacy, 2013; Grenard, Ames, & Stacy, 2013; Stacy, Ames, Ullman, Zogg, & Leigh, 2006). Given the successful application of WAT to a wide range of issues in health and cognition, it is important to fully understand the psychometric characteristics and construct validity of the measure. The IRT modeling framework provides one of the most comprehensive strategies available to accomplish these goals and has a number of advantages over the traditional classical test theory (CTT) approach (Reise, Ainsworth, & Haviland, 2005).
IRT Applied to Substance-Related WATIRT consists of a series of statistical models specified to describe the probability of endorsing an item as a function of an underlying latent trait (θ). In the context of the alcohol-related WAT, IRT describes the association between the probability that a participant generates an alcohol-related response to a given WAT item and his or her level of the latent alcohol-related implicit associative memory. The use of IRT in psychometric evaluation has several advantages over classical test theory (CTT). First, IRT allows for detailed investigation of WAT items in relation to the latent alcohol-related associative memory. It provides parameter estimates of item difficulty (b) and item discrimination (a). The b parameter indicates (1) how difficult a given WAT item is and (2) what level of latent memory association is needed so that 50% of participants would endorse an alcohol-related response to a given WAT item. Participants whose trait levels (i.e., alcohol-related associative memory) are higher are likely to generate an alcohol-related response to a WAT item with a higher b parameter value. The a parameter tells how effectively a WAT item differentiates among individuals with different levels of latent implicit alcohol-related associative memory. The item with a higher value of a is a good item because such an item discriminates effectively between individuals of slightly different levels of latent alcohol-related implicit associations in memory.
A critical advantage of IRT over CTT is that IRT is sample-invariant whereas CTT is sample-dependent (Hambleton & Jones, 1993). Under the situation in which an IRT model fits the data, the item parameter estimates (i.e., the a and b parameters) can be interpreted independent of the study sample (item-parameter invariance; Lord, 1980). Similarly, a latent trait can be estimated independent of a set of test items used in a study (person-parameter invariance; Lord, 1980). These sample invariant characteristics are not true in CTT. In CTT, item discrimination (i.e., item–total correlation), item difficulty (i.e., proportion of correct) and scale scores (i.e., the summed score) are completely dependent on a sample. Thus, an estimate of a latent trait score in CTT is largely affected by the characteristics of a study sample (Hambleton & Jones, 1993).
Another advantage of IRT is that reliability can be estimated with great flexibility. In CTT, a reliability estimate (e.g., Kuder–Richardson Formula 20, Cronbach’s coefficient alpha) is a fixed constant for all items. In contrast, reliability in IRT can be estimated at any point in the range of an underlying latent trait. Moreover, reliability estimates can be computed at both the item and test levels, using the item information and test information functions (IIF and TIF), respectively. In our substance-related WAT, we determined the extent to which each WAT item and WAT as a test accurately estimated a specific level of implicit substance-related associative memory.
Last, the IRT framework allows for the investigation of differential item functioning (DIF). The DIF analysis assesses whether or not a test item functions equivalently across subgroups of a study sample while controlling for the overall difference in the latent trait levels. For example, if the a or b parameter of a given WAT item is different between male and female participants with the same level of the latent implicit alcohol-related associative memory, the item is considered to exhibit DIF and could be a threat to the construct validity of the alcohol-related WAT (Kristjansson, Aylesworth, McDowell, & Zumbo, 2005).
Current StudyThe current study evaluated psychometric properties of two forms of a substance-related WAT, marijuana- and alcohol-related, using a unidimensional IRT modeling approach. The data were collected as part of a large-scale longitudinal study of dual-process theory and drug use in adolescents and consisted of 775 ethnically diverse, at-risk high school students in Southern California. The adolescent sample was chosen because of sufficient variability in alcohol, marijuana, and other drug use as well as the importance of this age group for the study of drug use progression.
The aims of the study were to (a) evaluate parameters of substance-related WAT items including item difficulty and item discrimination, (b) examine the precision of WAT at the item and test levels, (c) estimate the latent trait scores (i.e., the level of substance-related implicit associative memory) for each participant, and (d) evaluate criterion validity through the association between WAT scores and substance-use measures. Results of comprehensive psychometric validation of substance-related WAT will be discussed in light of the framework of a unified concept of construct validity (Messick, 1975, 1989, 1995). The comprehensive IRT approach illustrated in this article is applicable to a wide variety of measurement issues in associative memory and other areas of addiction and health behavior research.
Method Participants
The participants were 775 continuation high school (CHS) students (340 female) in the greater Los Angeles area. Their participation in this study did not require current or past history of substance use. The participants’ ages ranged from 14 to 20, of which 94% were between the ages of 15 and 18. The study sample comprised Hispanic (62.5%), Non-Hispanic White (12.5%), mixed race/ethnicity (18.7%), Black (3.2%), and other race/ethnicity that included Asian, Native American, and “other” (3.1%). They were recruited from classes from 42 CHSs, which were selected from over 100 CHSs in the region. The schools sampled did not provide any drug education programs to their students.
Measures
Word-association test
As described in the introduction, the substance-related WAT is an indirect memory test designed to assess the spontaneous retrieval of preexisting substance-related associations in memory (e.g., Stacy, 1997). The current study used two formats of WAT, an outcome-behavior association task (OBAT) and a compound-cue version of WAT. In OBAT, all cues are phrases that are related to affective outcomes of drug use (e.g., “feeling good”). In the compound-cue WAT, cues consist of either a combination of location and affective outcome phrases (two compound cues; e.g., “my bedroom, feeling good”) or a combination of situation, location, and affective outcome phrases (three compound cues; e.g., “weekend, friend’s house, having fun”). Fillers are cues that are unrelated to substance use (e.g., “doing homework”). Each of the three cue types had six target cues and two filler cues, totaling 18 target and six filler cues. Each trial started by visually presenting a cue phrase in the center of a computer display, and participants were instructed to respond with the first behavior or action that came to mind as quickly as possible. Responses were typed in a text box that appeared right below where the cue was presented. The next trial was generated by participants’ clicking a text button that reads “click here to continue” or after 21 seconds elapsed since the presentation of a cue, whichever came first.
The self-coding procedure (Frigon & Krank, 2009; Krank, Schoenfeld, & Frigon, 2010) was employed to code the WAT responses upon completion of the WAT session. In this procedure, participants were presented with a WAT cue and their typed response on the computer display, along with a list of 12 behavior categories (e.g., alcohol, marijuana, tobacco, exercise, etc.). They were asked to check one or more categories that were related to their responses. A checked response was coded 1 and an unchecked response was coded 0, and these scores were summed to yield a total WAT score for each category. In the current study, the scores for alcohol- and marijuana-related responses were examined separately.
Drug use: Marijuana and alcohol
Frequency of drug use was measured by a self-report drug-use questionnaire (Stacy et al., 1990; Stacy, 1997) that asked participants to indicate how many times they had used each drug in the past year and the past 30 days. The questionnaire was an 11-point rating scale, with frequency response options ranging from 1 (None) to 11 (91 + times). The reliability and validity of these self-reported drug-use measures were demonstrated elsewhere (e.g., Stacy et al., 1990).
Other variables
Participants’ demographics (age, gender, and language use), scores on the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989), and frequencies of simultaneous polydrug use (Collins, Ellickson, & Bell, 1998) were also assessed. These measures were used as predictors in missing data analyses reported below (see Data Analysis Plan for more details).
Procedure
We contacted each continuation high school (CHS) to arrange recruitment and obtained both written assent from eligible students and consent from their parents. Assent and consent forms explained that the purpose of the study was to investigate teenagers’ health behaviors, requiring their participation in three assessments over the course of two years to complete the study. Computer-based assessments were administered during regular school hours in groups of up to 20 (M = 11.67 participants per session) in a classroom that was provided by each CHS. Data collectors set up a mobile computer laboratory in each classroom that included 20 laptop computers supplied by the research project. Upon arrival to the laboratory, participants were randomly assigned to a computer. After the instructions were given, the assessments began by participants’ pressing any number key on the keyboard. The rest of the assessments were self-directed by the computer program. A session lasted an average of 60 minutes. Participants received a $10 movie ticket in exchange for their participation during Wave 1; the data reported in this article.
Data analysis plan
Evaluation of the psychometric properties of marijuana- and alcohol-related WAT items consisted of three steps. The analyses were conducted separately for each type of WAT.
IRT assumption checking
We tested two assumptions of the IRT model, unidimensionality and local independence, using both categorical confirmatory factor analysis (CCFA) and IRT methods. In assessing unidimensionality, we fit a one-factor CCFA model with weighted least-squares with mean and variance adjustment (WLSMV; Muthén, du Toit, & Spisic, 1997), by constraining all WAT items to load onto a single latent factor of implicit alcohol-related (or marijuana-related) associative memory. The model fit was evaluated according to the guidelines of Hu and Bentler (1999). A unidimensional two-parameter logistic (2PL) model was also fitted to the data to evaluate unidimensionality. We evaluated overall model fit by examining Maydeu-Olivares-Joe’s M2 (Maydeu-Olivares & Joe, 2006), a limited-information overall fit statistic, as well as the item-level model fit by assessing the S-χ2, an item misfit index (Orlando & Thissen, 2000).
The local independence (LI) assumption was evaluated by checking modification indices (MI) of residual covariances in the one-factor CCFA model and the local dependence (LD) statistics (Chen & Thissen, 1997) in the 2PL model. Potential local dependence (LD) is suspected when an excess correlation between a pair of items is observed after controlling for a single latent construct (Thissen & Steinberg, 2009). This suggests a violation of LI, implying to some investigators that the two items ask the exact same questions twice (Varni et al., 2010).
Differential item functioning
DIF tests were conducted to test for item invariance across gender and age groups. Two types of DIF were examined: Uniform DIF implies that the item exhibits a difference in the b parameter between two groups. Nonuniform DIF reflects a difference in the a parameter between two groups. Note that a group difference in DIF is examined while controlling for the overall group difference in the levels of the latent trait. The current study used a one-step Wald test (Cai, Thissen, & du Toit, 2011; Woods, Cai, & Wang, 2013), in which designated anchor items were used to link the latent trait metric for two groups. Anchor items were those items designated not to vary across groups. We identified the anchored items from a two-step Wald test (see Langer, 2008, for more detail) before conducting the one-step Wald test. In the one-step Wald test, a model fit was conducted in the following one-step manner: The mean and standard deviation (SD) of the reference group were fixed to 0 and 1, respectively, and the mean and SD of the focal group and the item parameters (the a and b parameters) were estimated at the same time. The item parameters for the designated anchor items were constrained to be equal between the two groups, whereas those for the candidate items were free to vary between the two groups. The software we used, flexMIRT (Cai, 2012), produces results of the Wald χ2 test for the comparisons of the candidate item(s) between the two groups. In comparisons between male and female participants, we used male participants as the reference group. In comparisons between younger (14–16 years old) and older participants (17 years old and above), the reference group was the young group.
IRT: Item-parameter estimation
We evaluated item parameter estimates of any alternative sets of WAT items suggested by the preceding analysis. Both 1PL and 2PL models were fitted to examine whether the a parameter should be fixed or varied across the WAT items. Further investigated was the amount of information each WAT item and the total WAT scale provided with respect to the latent trait. The item containing more information at a given level of the latent trait is considered more reliable. Latent trait scores for alcohol- and marijuana-related implicit associative memory were estimated separately as a function of various WAT-item scores, using expected a posteriori (EAP) estimation.
Criterion-related validity
Criterion-related validity coefficients of substance-related WATs were calculated by separately correlating marijuana- and alcohol-related WAT scores with respective drug-use frequencies from the past 30 days and 1 year. A nonparametric bootstrap method (Efron, 1979, 1987; Efron & Tibshirani, 1985) was used to estimate the Pearson correlation coefficients (termed r*) and their confidence intervals, as the assumption of bivariate normality was violated. We used a bias-corrected and accelerated (BCa) procedure (Efron, 1987) to construct confidence intervals for r* between the following pairs of variables: alcohol-WAT scores and alcohol use from the past 30 days; alcohol-WAT scores and alcohol use from the past year; marijuana-WAT scores and marijuana use from the past 30 days; and marijuana-WAT scores and marijuana use from the past year.
Missing Data
The missing data rates on the WAT ranged from 2% to 15% across 18 items, which was not unexpected with open-ended item formats. In the IRT analyses, list-wise deletion (LWD) of missing data was implemented. The use of LWD in IRT analyses is supported by several IRT simulation studies that have demonstrated acceptable-to-good parameter estimates of item discrimination and difficulty (Finch, 2008), no bias of uniform DIF detection with missing at random (MAR) data (Robitzsch & Rupp, 2009), and very close results to a complete data set (i.e., a data set with no missing data) in terms of power, Type I error rate, and effect sizes in the detection of nonuniform DIF (Finch, 2011).
In the criterion-related validity analysis that was conducted with psychometrically validated WAT items, multiple imputation (MI; Rubin, 1987) was used for missing data to obtain unbiased estimates of parameters. Multivariate imputation by chained equations (van Buuren, Boshuizen, & Knook, 1999; van Buuren & Oudshoorn, 2000) was used as the specific form of multiple imputation, applying the mice package (van Buuren & Groothuis-Oudshoorn, 2011) in the R statistical environment (R Development Core Team, 2012). This technique has recently gained popularity (Azur, Stuart, Frangakis, & Leaf, 2011) due to its ability to model each variable with missing data, regardless of its distribution (see van Buuren & Groothuis-Oudshoorn, 2011, for detailed procedures).
ResultsParticipants’ demographic variables and their alcohol and marijuana use are summarized in Table 1. To determine whether or not the school-cluster variables should be taken into account in subsequent analyses, we computed the design effect and intraclass correlation for alcohol and marijuana use among the average of 42 CHSs. A design effect of 2.0 was used as a cut-off (see Muthén & Satorra, 1995). The design effect (intraclass correlation in parentheses) for alcohol and marijuana use was 1.8 (.016) and 1.5 (.012), respectively. Thus, the school-cluster variable was not included in our analyses.
Demographic Variables and Substance Use
Alcohol-Related Word Association
IRT assumption checking
Both CCFA and 2PL models showed a good fit to the data, indicating unidimensionality of the 18 alcohol-related WAT items. Results from CCFA, conducted using Mplus, Version 6.11 (Muthén & Muthén, 2011), revealed fit indices as follows: comparative fit index (CFI) = .944, Tucker-Lewis Index (TLI) = .937, and root mean square error of approximation (RMSEA) = .045, with a 90% confidence interval (CI) of .036 to .054. All of the 18 factor loadings were significant (p < .01), ranging from .46 to .75. A 2PL model was fitted using flexMIRT, Version 1.0.4.3 (Cai, 2012) and indicated a good model fit (RMSEA = .04). Regarding the LI assumption, there were three potential item pairs with LD, implied by relatively large values of modification indices (MI) for residual covariances: (1) “friend’s house, feeling a rush” and “weekend, friend’s house, feeling a rush,” (2) “friend’s house, feeling a rush” and “feeling a rush,” and (3) “my bedroom, feeling good” and “my bedroom, feeling relaxed.” In the IRT analysis, no indication of LD item pairs (LD χ2 > 10) was obtained. Only one item (“weekend, party, feeling high”) showed a poor item fit (p < .0001). After examining the item contents, we set aside “friend’s house, feeling a rush” and “weekend, party, feeling high,” from a subsequent analysis. The model fit was slightly improved after removing these two items, CFI = .961, TLI = .955, and RMSEA = .037 (90% CI = .027 − .047).
DIF
The DIF test detected only one item exhibiting DIF across gender. The item “weekend, friend’s house, feeling a rush” discriminated more effectively for male (a = 2.51) than female participants (a = 1.27; p < .02). Thus, the item was excluded from the subsequent analysis. We also dropped “feeling high” because the discrimination parameter for the male group was substantially low (a = .78). With regard to age groups, the item “feeling more relaxed” was the only item with a significant uniform DIF (p < .02), indicating that this item was easier for older (b = 1.62) than younger participants (b = 2.31). However, we expected that some items might be more difficult at younger ages while still being potentially applicable to later changes with increasing age. Thus, the item remained in the analysis.
IRT: Item parameter estimation
Our revised alcohol-related WAT, reduced to 14 items (α = .80), was fitted with both 1PL and 2PL models. Both models indicated a good fit (RMSEA = .04 and .03 for 1PL and 2PL, respectively), with no evidence of a violation of LI. A likelihood ratio test revealed a significant improvement in fit by the 2PL, relative to the 1PL, G2 (17) = 42.27, p < .001. These results indicated that alcohol-WAT data were reproduced by the model better when the a parameters were estimated freely (2PL), rather than being constrained to be equal (1PL). Table 2 presents the estimated parameters for both models. The common a parameter in 1PL was 2.02. In 2PL, the a parameters ranged from 1.62 to 2.46, indicating that all 14 alcohol WAT items effectively differentiated the participants across different levels of the latent trait. The b parameters in both models were very similar for each item. For most of the WAT items, moderate to strong levels of implicit alcohol-related memory associations were needed to endorse alcohol-related responses. All of these parameter estimates are graphically represented in the item-characteristic curves (ICC; Figure 1).
Item Parameter Estimates for 1PL and 2PL in the Alcohol- and Marijuana-Related WAT
Figure 1. Item characteristic curves for the revised alcohol (solid) and marijuana (dashed) WAT items. The x-axes show the level of theta (latent implicit alcohol- or marijuana-related associative memory), with 0 representing the average level of theta. The y-axes show the proportion of alcohol- or marijuana-related responses generated to each WAT cue.
All ICCs show that the probability of endorsing alcohol-related responses was low for those participants whose latent trait levels were below 1.0. The slopes of most WAT items were steepest throughout the range of the latent levels from about 1.0 to 2.0. These items also provided most information about the latent trait (i.e., most reliable) in this range of the latent level (see Figure 2). The amount of information provided by each WAT item was summed to create the test information curve (TIC; see Figure 3), which demonstrates that the alcohol-related WAT is most reliable at moderate-to-high levels of the latent trait. Estimated latent trait scores (see Table 3) revealed that those who endorsed one alcohol-related response were estimated to possess an average level of latent alcohol-related associative memory. As participants endorsed more alcohol-related responses, their latent score increased.
Figure 2. Item information functions for the revised alcohol (solid) and marijuana (dashed) WAT items. The x-axes show the level of theta (latent implicit alcohol- or marijuana-related associative memory), with 0 representing the average level of theta. The y-axes show the amount of information (I), an index of how accurately each item contributes to estimate the latent trait at a given level of theta.
Figure 3. Test information functions (TIFs) for the revised alcohol (solid) and marijuana (dashed) WATs. The x-axis shows the level of theta (latent implicit alcohol- or marijuana-related associative memory), with 0 representing the average level of theta. The y-axis shows the amount of information (I), an index of how accurately each form of WAT estimates the latent trait at a given level of theta. I = 5, 10, and 20 is equivalent to a reliability estimate of .80, 90, and .95, respectively.
Alcohol- and Marijuana-Related WAT Scores, Latent Trait Scores (EAP) and Their Standard Deviations
Marijuana-Related Word Association
IRT assumption checking
CCFA showed a good fit of the marijuana-model, CFI = .978, TFI = .975, RMSEA = .043 (90% CI = .034–.052). IRT analysis revealed an adequate model fit (RMSEA = .05) with no indication of a poor item fit. Thus, the marijuana-related WAT was determined unidimensional. With regard to LI, both CCFA and 2PL detected only one potential LD item pair, “feeling high” and “my bedroom, feeling high.” After reviewing the item contents, we removed the latter item from the analysis.
DIF
For gender, two items were detected as having uniform DIF: “feeling a rush” (p = .02) and “forgetting problems” (p = .03). The endorsement of the marijuana-related response “Feeling a rush” was easier for females (b = 1.59) than for males (b = 2.03) after matching the two groups on the latent trait. Conversely, “forgetting problems” was easier for males to endorse (b = 1.18) than for females (b = 1.44). These two items were removed from the subsequent analyses. As for age groups, no items exhibited a significant DIF.
IRT: Item parameter estimation
The revised marijuana-related WAT had a total of 15 items (α = .87). Both 1PL and 2PL models fit the data adequately (RMSEA = .05) with no sign of LD item pairs. A likelihood ratio test showed that 2PL had a significantly better fit than 1PL, G2 (17) = 77.88, p < .001. Estimated item parameters by both models and ICCs (2PL only) are presented in Table 2 and Figure 1, respectively. In 2PL, the a parameter varied from 1.37 to 3.62 and the b parameters ranged from −.48 to 2.05. As shown in Figure 2, IIFs show that “feeling high” was the only item most reliable at the below-average level of the latent trait. Still, TIF illustrates that the marijuana WAT was most reliable around the moderate-to-high levels of the latent trait continuum (see Figure 3). Estimated latent trait scores showed that a total marijuana WAT score of 3 corresponded to the average level of the latent marijuana-related associative memory. A monotonically increasing relationship was observed between the total WAT scores and the latent trait scores (see Table 3).
Criterion-Related Validity
Table 4 shows the correlations between substance-related WAT scores and drug-use frequencies. In both alcohol- and marijuana-related WATs, the participants who endorsed more substance-related responses tended to report higher frequencies of substance uses both in past year use (r* = .44 [BCa CI = .37–.51] and .56 [BCa CI = .50–.61], for alcohol and marijuana, respectively) and past 30-days use (r* = .38 [BCa CI = .30–.47] and .48 [BCa CI = .42–.54], for alcohol and marijuana, respectively).
Bivariate Correlations (BCa 95% CI) Between the Substance-Related WAT Scores and Frequencies of Past Drug Use
DiscussionThe present study was the first to apply a comprehensive psychometric framework using IRT approaches to evaluate psychometric properties of alcohol- and marijuana-related WATs in a sample of ethnically diverse, at-risk adolescents. Our results have demonstrated that both forms of WAT have good psychometric properties when subjected to a comprehensive latent variable and IRT analyses. The discussion below focuses on key findings regarding item and scale properties as well as evidence of construct validity (Messick, 1989, 1995).
Alcohol- and Marijuana-Related WAT: Scale Properties
The original 18 WAT items were reduced to 14 and 15 items in alcohol- and marijuana-related WAT, respectively. Items were removed because they exhibited poor item fit (two items each in both WATs), LD issues (one item each in both WATs), or gender bias (one item in the alcohol WAT and two items in the marijuana WAT). Excluding these items improved the revised versions of the substance-related WAT. As expected, both forms of WAT were shown to be unidimensional and most reliable with individuals with moderate-to-high levels of latent alcohol- or marijuana-related associative memory (see Figure 3). A monotonically increasing relationship between the total WAT scores and estimated latent trait scores was observed in both WATs (see Table 3). These results confirmed that the substance-related WATs measure a single construct of substance-related associative memory, as it purports to do. Furthermore, the total alcohol- and marijuana-WAT scores were positively correlated with frequencies of respective past substance-use behaviors, providing strong evidence of criterion-related validity. This finding is in agreement with that of Krank et al. (2010), who reported that self-coded WAT scores were positively associated with past 30-days alcohol use among college students and added to evidence supporting the use of self-coded scoring procedures (Frigon & Krank, 2009; Krank et al., 2010).
Alcohol- and Marijuana-Related WAT: Item Properties
Item discrimination for all items in both WATs showed high discrimination parameters (a > 1.35). Among the items, some of the compound cues exhibited very high discrimination parameter values, especially in the marijuana-related WAT. Those compound cues included “friend’s house, having fun” (a = 3.62), “friend’s house, hanging out, feeling good” (a = 3.14), and “Friday night, friend’s house, having fun” (a = 3.20). A possible explanation for this is that when a positive affective outcome cue was combined with a peer cue to create a compound cue, its item discrimination was further improved. This explanation is consistent with some theories of adolescent substance use that focus on peer influence as a pivotal risk factor (e.g., Hawkins, Catalano, & Miller, 1992; Petraitis, Flay, & Miller, 1995).
With respect to item difficulty, most of the WAT items were most reliable at the moderate-to-high levels of the latent construct. However, a slightly different pattern of results was observed across the two WATs. In the alcohol WAT, all but one item (“Friday night, friend’s house, having fun”) had item-difficulty parameter estimates greater than 1.0. This indicates that the probability of endorsing alcohol-related responses to the items was lower than .5 for the participants with below moderate levels of latent alcohol-related associative memory. In contrast, the marijuana-related WAT contained a mix of items with moderate-to-high difficulty parameters and items with lower difficulty parameters. This led the marijuana WAT to cover a wider range of the latent-trait continuum than the alcohol WAT. For example, even among those participants with a lower level of latent marijuana-associative memory, half of them endorsed marijuana-related responses to the cues, “feeling high” (b = −.48) and “weekend, party, feeling high” (b = .20). On the other hand, these two cues were not good ones for alcohol. Both items showed a poor model fit and hence were excluded from the revised alcohol-WAT. Further, the only item with the phrase “feeling high” in the revised alcohol-WAT had a high difficulty parameter estimate (“my bedroom, feeling high,” b = 1.97). Hence, we consider “feeling high” as a cue strongly associated with marijuana, particularly at a lower range of the latent trait. This suggests that inclusion of behavior-specific cues may further improve the psychometric properties of substance-related WAT.
Alcohol and Marijuana-Related WAT: Unified Concept of Validity
Traditionally, construct validity has been examined by use of multitrait-multimethod matrix (MTMM matrix, Campbell & Fiske, 1959) or confirmatory factor analysis (CFA, Jöreskog, 1969; Kenny & Kashy, 1992; Stacy, Widaman, Hays, & DiMatteo, 1985) procedures to gather evidence of convergent and discriminant validity. In contrast, Messick (1989, 1995) suggested six aspects of construct validity, arguing that construct validity of a measurement instrument should be justified by use of the available evidence for a wide variety of aspects of construct validity, including content, substantive, structural, generalizability, external, and consequential aspects. The current study showed that the substance-related WATs exhibited evidence of each of these aspects of construct validity. For example, the content aspect is evidenced by the fact that all substance-related WAT items were selected from, or created based on, past studies that reported the utility of WAT as a measure of substance-related implicit associative memory (e.g., Ames et al., 2007). A unidimensional structure of both forms of WATs supports the structural aspect of construct validity, indicating that a single construct of alcohol- or marijuana-related implicit associative memory is evaluated in the WAT. Regarding the substantive aspect, which requires empirical evidence of response consistencies from data, both forms of WAT revealed good internal consistency across a range of the latent trait. For example, the amount of information (I) exceeded 5.0, which is equivalent to a reliability estimate of .80, at the underlying latent trait levels between 0 and 2.0 (see Figure 3). In terms of the generalizability aspect of construct validity, the DIF tests demonstrated that all items in the revised version of the alcohol- and marijuana-related WATs were invariant across gender and age groups. Finally, although we were not able to investigate any evidence of convergent and discriminant validity in the current study, the obtained evidence of criterion-related validity for both forms of WAT justifies the external aspect of construct validity. As reported above, a significant correlation was found between substance-related WAT scores and frequencies of substance use, both in the past 30 days and the past year. Overall, the current study revealed multiple lines of evidence for the construct validity of the alcohol- and marijuana-related WAT, in accord with the unified concept of construct validity (Messick, 1989, 1995).
Limitations
Several caveats in the present study need to be addressed. First, item invariance was examined only across gender and age groups due to the limited number of samples representing different subgroups (e.g., ethnicity). Thus, the current WAT items might have shown DIF across other subgroups. Future investigations that explore DIF of substance-related WAT items could be conducted across ethnicity and other defining characteristics. Second, because the data were cross-sectional, the direction of the possible causal relationship between WAT scores and past drug use was not inferred. Last, drug-use behavior was measured via a self-report questionnaire, thus responses are sensitive to demand characteristics and/or social desirability bias. However, under circumstances in which adolescents were assured that responses would be confidential, adolescent self-reports have been shown to be accurate (Dent, Sussman, & Stacy, 1997; Donohue, Hill, Azrin, Cross, & Strada, 2007).
ConclusionDespite these limitations, the present study revealed sound psychometric properties of the alcohol- and marijuana-related WAT. Both forms of WATs were most reliable at moderate-to-high levels of the underlying implicit alcohol- or marijuana-related associative memory. Knowledge of the level of reliability at different levels of the latent trait is one of the several fundamental advantages of IRT over traditional psychometric evaluation (e.g., CTT), in addition to advantages of sample invariance, flexibility, and rigor in evaluating differential item functioning. The IRT and construct validation procedures shown here are useful for a wide range of research topics in addiction as well as basic cognitive research on WAT. Although the procedures can be applied to any presumed measures of an underlying trait, it may be surprising that these highly quantitative procedures can be effectively applied to responses that are self-generated and open-ended—the responses are essentially qualitative in origin. When such responses are amenable to numeric coding, they can be usefully integrated into formal and comprehensive tests of psychometrics and construct validity as revealed here.
Footnotes 1 In several recent studies by other investigators on continuation high schools (CHSs) in the greater Los Angeles area (e.g., Barnett et al., 2013; Sussman, Sun, Rohrbach, & Spruijt-Metz, 2012), sample characteristics (including the male-to-female ratio, the mean age, racial/ethnic profile, and past alcohol and marijuana use) were very similar to those in the current study. Although demographic information was not available on all CHSs in the region, the general consistency across diverse studies in the region suggests that the present sample is at least similar to other samples previously drawn from the population.
2 Although it may appear that the alcohol- and marijuana-related WATs should be analyzed by a multidimensional IRT approach, we used a unidimensional approach, as we used the same set of items for both substances. The use of overlapping items was inevitable to take into account individual differences in substance-related associative memory, as has been evidenced in previous studies (see Stacy, Galaif, Sussman, & Dent, 2006; Sussman, Stacy, Ames, & Freedman, 1998). A compensatory multidimensional model was also not relevant because a WAT response related to one substance should not be compensated by one’s level on the construct of the second substance. Thus, the two forms of WATs were analyzed separately.
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Submitted: May 9, 2013 Revised: December 20, 2013 Accepted: December 27, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (3), Sep, 2014 pp. 852-862)
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Record: 6- Application of the social action theory to understand factors associated with risky sexual behavior among individuals in residential substance abuse treatment. Reynolds, Elizabeth K.; Magidson, Jessica F.; Bornovalova, Marina A.; Gwadz, Marya; Ewart, Craig K.; Daughters, Stacey B.; Lejuez, C. W.; Psychology of Addictive Behaviors, Vol 24(2), Jun, 2010 pp. 311-321. Publisher: American Psychological Association; [Journal Article] Abstract: Risky sexual behavior (RSB) is a leading cause of HIV/AIDS, particularly among urban substance users. Using the social action theory, an integrative systems model of sociocognitive, motivational, and environmental influences, as a guiding framework, the current study examined (1) environmental influences, (2) psychopathology and affect, (3) HIV-related attitudes and knowledge, and (4) self-regulatory skills/deficits as factors associated with event-level condom use (CU) among a sample of 156 substance users residing at a residential substance abuse treatment center (M age = 41.85; SD = 8.59; 75% male). RSB was assessed using event-level measurement of CU given its advantages for improved accuracy of recall and ability for an examination of situational variables. A logistic regression predicting event-level CU indicated the significant contribution of partner type (environmental influences), less favorable attitudes towards condoms (HIV-related attitudes and knowledge), and higher levels of risk-taking propensity (self-regulatory skills/deficits) in predicting greater likelihood of not having used a condom at one's most recent sexual encounter. This study contributes to the literature examining HIV risk behaviors among substance users within a theory-driven model of risk. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Application of the Social Action Theory to Understand Factors Associated With Risky Sexual Behavior Among Individuals in Residential Substance Abuse Treatment
By: Elizabeth K. Reynolds
Center for Addictions, Personality, and Emotion Research and Department of Psychology, University of Maryland, College Park;
Jessica F. Magidson
Center for Addictions, Personality, and Emotion Research and Department of Psychology, University of Maryland, College Park
Marina A. Bornovalova
Department of Psychology, University of Minnesota
Marya Gwadz
The Center for Drug Use and HIV Research, National Development and Research Institutes, New York
Craig K. Ewart
Department of Psychology, Syracuse University
Stacey B. Daughters
School of Public Health, University of Maryland, College Park
C. W. Lejuez
Center for Addictions, Personality, and Emotion Research and Department of Psychology, University of Maryland, College Park
Acknowledgement: This work was supported by NIDA Grant R01 DA19405 and NIDA Grant 1 F31 DA023302–01A1. NIDA had no further role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
Despite advances in HIV prevention efforts, an estimated 1.1 million individuals are living with HIV/AIDS in the United States (Centers for Disease Control and Prevention, 2008), and approximately 56,300 new infections occurred in 2006 (Hall et al., 2008). Risky sexual behavior (RSB) is currently the most common means of acquiring HIV; approximately 83% of new infections are acquired through sexual transmission (Centers for Disease Control and Prevention, 2008). Individuals who engage in illicit substance use are especially vulnerable to the contraction of HIV through RSBs (e.g., Hoffman, Klein, Eber, & Crosby, 2000). Risk is increased further when considering individuals living in low-income urban areas where HIV risk factors including injection drug use and prostitution occur at higher rates than most other settings (Adimora, & Schoenbach, 2005; Rhodes, Singer, Bourgois, Friedman, & Strathdee, 2005). Given the elevated HIV risk via sexual transmission in urban, substance using populations, there is a clear need to understand the processes underlying RSB in this specific population.
In previous research, a variety of variables have been found to be related to RSB including demographic/background variables such as age, gender, ethnicity, and education (e.g., Avants, Marcotte, Arnold, & Margolin, 2003; Ensminger, Anthony, & McCord, 1997; Johnson, Cunningham-Williams, & Cottler, 2003; Miller & Neaigus, 2002), HIV status (Kalichman, Rompa, & Webster, 2002; Semple, Patterson, & Grant, 2000), past experience of sexual or emotional abuse (Arriola, Louden, Doldren, & Fortenberry, 2005; Senn, Carey, Vanable, Coury-Doniger, & Urban, 2006), disinhibition (Hayaki, Anderson, & Stein, 2006; Lejuez, Simmons, Aklin, Daughters, & Dvir, 2004), negative emotionality and affect (Lehrer, Shrier, Gortmaker, & Buka, 2006; Mustanski, 2007), psychopathology (Johnson et al., 2003; McMahon, Malow, Devieux, Rosenberg, & Jennings, 2008), general self-esteem (Semple, Grant, & Patterson, 2005), partner type (Macaluso, Demand, Artz, & Hook, 2000), and attitudes towards condoms (Somlai et al., 2000). This body of work provides important insight into understanding RSB among urban substance users; yet, there remain open directions for future inquiry. Specifically, there is a need to utilize a theoretical framework to understand the interplay of key variables underlying RSB among urban substance users.
In considering frameworks to understand and intervene on RSB, social–cognitive based models most often have been applied and used to guide interventions. Social–cognitive theory highlights ways in which peoples' beliefs about their personal capabilities, or self-efficacy, influence their attempts to alter ingrained habits (Bandura, 1986). Social–cognitive informed interventions most frequently include the provision of basic HIV information, risk personalization, modeling, skills building (including problem solving and relapse prevention) and to a lesser extent social support enhancement (van Empelen et al., 2003). Although these social–cognitive interventions have been shown to be relatively successful in reducing RSB among drug users (see van Empelen et al., 2003, for a review), there is a clear need for further improvement. Specifically, researchers have identified that the social–cognitive models have failed to account for self-regulatory processes and contextual factors (Bagozzi, 1992; Gollwitzer, 1990; Schwarzer, 1992). Yet, these internal and environmental context factors need to be altered or mobilized to support health risk behavior self-change (Ewart, 2009) and may be of particular importance for urban substance users considering noted difficulties in self-regulation (Palfai, 2006) and high environmental risk (Adimora & Schoenbach, 2005).
Moving in this direction, the social action theory (SAT), an integrative systems model of social-motivational, cognitive, and environmental processes, may provide a novel and potentially productive framework for understanding RSB among urban substance users (Ewart, 1991, in press). SAT was developed as an extension of individual-level psychological theories to address the broad complexities of public health problems. The overarching goal is the detection and manipulation of environmental and self-regulatory skills/deficits that can promote health and/or hinder health behaviors and habits (Johnson, Carrico, Chesney, & Morin, 2008). Initial conceptualizations of SAT focused on behavioral health more broadly defined. From these original roots, SAT has been applied to HIV risk reduction and the prevention of HIV risk behavior (Gore-Felton et al., 2005; Lightfoot, Rotheram-Borus, Milburn, & Swendeman, 2005). For example, previous work has identified the domains of SAT, including contextual and self-regulatory skills/deficits, to be predictive of youth sexual behavior (Mellins, Dolezal, Brackis-Cott, Nicholson, & Meyer-Bahlburg, 2007).
SAT elaborates on existing HIV-related social–cognitive models that emphasize cognitive appraisals and beliefs (e.g., Bandura, 1994; Fisher, Fisher, Williams, & Malloy, 1994) by focusing on social motivational and contextual influences that energize and shape behavior, specifically highlighting the ways in which important self-goals, frequently practiced routines, social-emotional competence, and social power affect substance use and other risky behaviors (Ewart, in press; Lightfoot et al., 2005). Applied to RSB, SAT views the choice of engaging in RSB as influenced by the personal regulatory resources and social power afforded by environmental context (variables that have historical impact on the individual as well as those relevant in the immediate context of the RSB) in combination with the individual's psychopathology and affect, HIV-related attitudes and knowledge, and self-regulatory skills/deficits.
Using SAT as a guiding framework, the current study sought to examine variables associated with event-level RSB among residents of an urban, substance-use treatment facility. In addition to demographic variables and HIV status, predictor variables used for this study were derived from four key domains of SAT: (1) environmental influences (i.e., childhood trauma and characteristics of last sexual intercourse), (2) forms of psychopathology and affect (substance use, depression, anxiety, borderline and antisocial personality disorders, and negative emotionality), (3) HIV-related attitudes and knowledge (condom attitudes and HIV knowledge), and (4) self-regulatory skills/deficits (trait nonplanning impulsivity, delay discounting, and risk-taking propensity). From these more general domains it is important to consider specific variables that capture the relevance of SAT to RSB.
To assess environmental influences on RSB, we utilized a measure of historical experience that would capture physical, emotional, and sexual childhood abuse. We also assessed the characteristics of the last sexual intercourse (using a measure adapted from Tortu, McMahon, Hamid, & Neaigus, 2000) that included variables such as most recent partner type, whether this partner is an injection drug user, HIV positive, and/or having sexual intercourse with other people, as well as whether the participant was drunk or high at the last sexual encounter. These two measures capture historical as well as current environmental influences that may dispose one to engage in RSB by shaping social motives, self-regulatory skills/deficits, and social power in risky situations.
The components of the second domain of SAT, forms of psychopathology and affect, are conceptualized as factors that may interfere with one's ability to self-regulate (i.e., engage in safe sexual practices). To assess psychopathology and affect, we used the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM–IV; American Psychiatric Association), nonpatient version (SCID–IV–NP; First, Spitzer, Gibbon, & Williams, 2001) to assess for the presence of Axis I and Axis II disorders that may be related to RSB engagement. Specifically, the diagnoses most related to internal-affective states included mood and anxiety disorders, borderline personality disorder (BPD), and antisocial personality disorder (ASPD). In addition to diagnostic status, we also included continuous measures of substance use frequency as well as negative emotionality. Coupling diagnostic information with continuous information related to substance use frequency and affect captures a full picture of the psychopathology/affect domain conceptualized by SAT as being related to RSB.
To assess forms of knowledge and attitudes related to HIV, we utilized the Attitudes Toward Condom Scale (Somlai et al., 2000), which assesses not only negative attitudes related to condom use, but also perceived barriers related to condom nonuse. We also assessed HIV knowledge using an adapted survey from the Center for AIDS Intervention Research (CAIR; see Somlai et al., 2000), which assessed participants' understanding of HIV/AIDS risk, risk reduction steps, and condom usage. The combination of assessing condom attitudes and HIV knowledge encompasses the third domain of SAT related to RSB.
In the assessment of the final domain of SAT, self-regulatory skills/deficits, we used a battery of disinhibition measures. We included trait nonplanning impulsivity (which assesses not planning for the future, lack of self-control, and intolerance of cognitive complexity) given its link to self-regulatory skills/deficits defined by the SAT. We assessed delay discounting using the Delay Discounting Task (DDT; Kirby, Petry, & Bickel, 1999), which captures the degree to which an individual shows preference for either small, immediate rewards or larger, delayed rewards, and fits with the social-emotional competence component of SAT given its relation to factors associated with condom use decision making. To supplement self-report assessment with behavioral assessment of disinhibition, we also utilized the Balloon Analogue Risk Task (BART; Lejuez et al., 2002) to assess risk-taking propensity, a variable previously demonstrated to be associated with RSB in this type of sample (Lejuez et al., 2004). SAT considers RSB to result from self-regulatory skills/deficits as they relate to motivation and problem solving (Ewart, 1991, in press); this battery of behavioral and self-report measures of disinhibition address the self-regulatory skills/deficits delineated by SAT to be related to RSB.
Regarding our main outcome, RSB is often assessed using global association measures that include questioning one's general frequency of RSB during a given period of time (e.g., 6 months; Ross, Hwang, Zack, Bull, & Williams, 2002; Sanchez, Comerford, Chitwood, Fernandez, & McCoy, 2002; Somlai, Kelly, McAuliffe, Ksobiech, & Hackl, 2003). Although useful in many respects, global assessment of past frequency of RSB is subject to inaccuracy of recall and gross frequency count biases (Chawarski, Pakes, & Schottenfeld, 1998). Further, this method of assessment provides little information in terms of situational or context-specific conditions, resulting in condom use being treated a context-independent outcome (Kiene, Barta, Tennen, & Armeli, 2009; Weinhardt & Carey, 2000).
To address the limitations of global assessment of RSB, the utilization of event-level measurement (Leonard, & Ross, 1997; Temple, & Leigh, 1992) has been introduced into research concerning HIV risk behavior (e.g. Gillmore et al., 2002; Tortu et al., 2000). Event-level measurement typically assesses particular aspects of a recent sexual event, such as type of partner and whether one was under the influence of alcohol. In event-level measurement, participants describe behavior during a particular event rather than report general trends or averages of behavior (LaBrie, Earleywine, Schiffman, Pederson, & Marriot, 2005). Despite the potential limitations of underreporting due to forgetting of events (Schroder, Carey, & Vanable, 2003) and the lack of estimate of overall likelihood of RSB, event-level measurement is thought to avoid overreporting and rounding and allows for an examination of situational variables (Kiene et al., 2009).
Few studies have used this event-level methodology with at-risk samples of substance users and within a more comprehensive theory-driven model of risk. Indeed, event-level studies largely are limited to samples including college students (e.g., Brown, & Vanable, 2007; LaBrie et al., 2005) and nonsubstance dependent adult samples (e.g., Ibañez, Van Oss Marin, Villarreal, & Gomez, 2005; Leonard, & Ross, 1997). Of the studies that have utilized event-level data among substance users (e.g., Leigh, Ames, & Stacy, 2008; Scheidt, & Windle, 1996; Tortu et al., 2000; Watkins, Metzger, Woody, & McLellan, 1993), the predictors have largely focused on the effect of intoxication, sexual history (Scheidt, & Windle, 1996), and drug use severity (Scheidt & Windle, 1996; Watkins et al., 1993) and have rarely been incorporated into a broader model of risk. In sum, the current study examined four components of SAT in relation to event-level CU among a sample of substance users in residential substance abuse treatment.
Method Participants
Participants included 161 men and women receiving treatment at the Salvation Army Harbor Light residential substance abuse treatment facility located in Northeast Washington, DC. Five individuals were found to meet DSM–IV criteria for psychosis and thus were excluded from further analyses. The final sample (n = 156) ranged in age from 18 to 60 years, with a mean age of 42.85 years (SD = 8.59). Of the sample, 54.6% were crack/cocaine dependent, 15.1% heroin dependent, 10.6% marijuana dependent, and 34.3% alcohol dependent (of note adds up to more than 100% due to the fact that participants could be dependent on more than one drug). There were 25.0% who did not meet any dependence criteria, 44.3% were dependent on one drug, 23.6% were dependent on two drugs, 5.0% were dependent on three drugs, and 2.1% were dependent on four drugs. With regard to racial/ethnic background, 86.5% of the participants were African American, 7.1% were White, 1.9% were Hispanic/Latino, 1.3% were Native American, and 3.2% reported “other.” In terms of highest education level, 20.0% had not completed high school, 40.0% completed high school (or received a GED), and 40.0% completed at least some college, technical, or trade school. The majority of the sample reported current unemployment, and 53.9% reported a household income of less than $10,000 a year. Patients entered the treatment center either voluntary or under a pretrial-release-to-treatment program through the District of Columbia Pretrial Services Agency (53.3%). This program offers drug offenders who are awaiting trial the option to receive substance abuse treatment as a way to ensure appearance in court, provide community safety, and address an underlying cause of recidivism. Patients were contracted to a specific length of stay on entry into the treatment center. For the current sample, contract lengths included 30 days (41.4%), 60 days (29.7%), 90 days (6.3%), or 180 days (22.6%).
Procedures
Assessment sessions were held on Friday afternoons in a private room at the Salvation Army Harbor Light facility. Residents at the treatment center were approached and asked if they would be willing to participate in a study examining sexual behavior among substance users. They were told that the session would last up to 3 hr and that they would be paid $25 in the form of a grocery store gift card on discharge from treatment. Interested participants were given a more detailed explanation of the procedures and asked to provide written informed consent approved by the University of Maryland Institutional Review Board. Given issues of reading comprehension, efforts were made to ensure that participants understood all facets of the consent form and the study itself. Next, a diagnostic interview, the SCID–IV–NP (First et al., 2001), was administered in a separate private room by a trained senior research staff member. If not eligible following the SCID–IV–NP due to the presence of psychotic symptoms, participants were debriefed and paid $10 in grocery gift cards for their time. Following completion of the diagnostic interview, the participants completed a battery of questionnaires (described in detail below).
Although the participants were completing the questionnaires, individuals trained in administering the behavioral task took participants one by one into an adjacent room where they completed the computer task (i.e., BART). The order of completion of the questionnaires was counterbalanced across participants. For entry into the treatment center (independent of the current study), individuals were required to evidence abstinence from all substance use and to have completed a detoxification program if needed. As a result, acute drug effects likely did not affect the participants' scores on the testing battery. Once the participants completed each aspect of the assessment session (i.e., clinical interview, questionnaire packet, and computer task), they were told how much money they earned and signed a receipt. This payment ($25 in grocery store gift card) was deposited into their account at Harbor Light, which they received on discharge from the residential treatment center.
Measures
Event-level assessment of CU (event-level CU)
Participants reported whether a condom was used during their last sexual intercourse. This question, based on work by Tortu et al. (2000), served as the dichotomous dependent variable (coded: 0 = condom used, 1 = condom not used).
Demographic information and HIV status
A short self-report questionnaire was administered to obtain age, gender, race, education level, marital status, and total yearly income. In addition, participants reported their HIV status.
SAT derived independent variables
Domain 1: Environmental influences
Childhood trauma
As a measure of childhood trauma, we used the three abuse subscales (physical, emotional, and sexual) of the short form of the Childhood Trauma Questionnaire (Bernstein, Stein, & Newcomb, 2003). The CTQ–SF assesses childhood maltreatment experiences (i.e., “while you were growing up”) using a 5-point scale ranging from 1 (never true) to 5 (very often true). The CTQ–SF shows convergent and discriminant validity with other trauma measures (Bernstein et al., 1994; Fink, Bernstein, Handelsman, Foote, & Lovejoy, 1995). Internal consistency for the scale was adequate with Cronbach's α = .94.
Characteristics of last sexual intercourse
Based on Tortu et al. (2000), we administered a 10-item questionnaire on one's most recent sexual intercourse. Contextual predictors of CU from this measure included: type of partner that was involved in the most recent sexual intercourse encounter (commercial: defined as money or drugs being exchanged for sex; casual: defined as no committed relationship; or regular: defined as a committed relationship including a boyfriend/girlfriend or spouse), as well as whether this most recent partner was an injection drug user, HIV positive, and/or having sexual intercourse with other people. Further, the respondent was asked to indicate whether at their last sexual encounter they were drunk and/or high, and if they also engaged in oral or anal sex. Finally, the questionnaire asked whether the sexual encounter occurred in the last year.
Domain 2: Psychopathology and affect
Psychopathology
The SCID–IV–NP (First et al., 2001) was used to determine the presence of current Axis I disorders (i.e., clinical disorders) including depression, anxiety (presence of any anxiety disorder including panic, social phobia, generalized anxiety disorder, specific phobia, posttraumatic stress disorder), psychosis (for exclusion criteria), and substance dependence as well as Axis II disorders (i.e., personality disorders) including borderline personality disorder (BPD) and antisocial personality disorder (ASPD). This measure has demonstrated reliability (First et al., 2001). All eligible participants were administered the interviews in a private area by trained research staff.
Substance use frequencies
Frequency of drug (i.e., marijuana, heroin, and crack/cocaine) and alcohol use was assessed with a standard substance use questionnaire modeled after the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). Specifically, participants were asked how often they used each substance in the past year prior to treatment. Response options were: 0 (never), 1 (one time), 2 (monthly or less), 3 (2 to 4 times a month), 4 (2 to 3 times a week), and 5 (4 or more times a week). This measure was used to supplement dependence diagnoses from the SCID–IV–NP with more detailed information on substance use frequency.
Negative emotionality
The Multidimensional Personality Questionnaire Brief Form (MPQ–BF; Patrick, Curtin, Tellegen, 2002) is a 155-item version of the original 300-item MPQ (Tellegen, 1982) developed to assess a variety of personality traits and temperamental dispositions. The higher order factor of Negative Emotionality (NEM) is comprised of the traits of Stress Reactivity, Alienation, and Aggression, and we utilized only this subscale given its previously demonstrated link to RSB (Lehrer et al., 2006). Internal consistency for the scale was good (α = .89).
Domain 3: HIV-related attitudes and knowledge
Condom attitudes
The Attitudes Toward Condoms Scale (Somlai et al., 2000) consists of eight items focusing on negative attitudes regarding CU and beliefs concerning barriers to CU, such as perceptions and connotations of CU, perceived unreliability of condoms in preventing sexually transmitted diseases, and embarrassment when purchasing condoms. The scale allows for level of agreement with each item using a 6-point Likert scale ranging from 1 (strongly disagree) to 6 (strongly agree). A participant's negative attitudes toward condoms scale score reflects the mean of the eight items, with higher values reflecting more negative attitudes toward CU. Internal consistency for the scale was acceptable (α = .66).
HIV knowledge
Adapted from a survey developed by CAIR (see Somlai et al., 2000), participants' understanding of HIV/AIDS risk, risk reduction steps, condom usage, and safer sex practices was assessed using a 22-item true–false scale. Scores on this scale range from 0 to 22, reflecting the number of questions correctly answered. Internal consistency for the scale was acceptable (α = .67).
Domain 4: Self-regulatory skills/deficits
Trait nonplanning impulsivity
Trait impulsivity was assessed using the Barratt Impulsiveness Scale (Version 11, BIS–11; Patton, Stanford, & Barratt, 1995). The BIS–11 is a 30-item, self-report questionnaire that contains three subscales, which have been termed Motor Impulsiveness, Cognitive Impulsiveness, and Nonplanning. We utilized only the Nonplanning subscale, which we conceptualized as being most relevant to condom nonuse given its focus on a lack of planning for the future, specifically targeting lack of self-control and intolerance of cognitive complexity. The questions require participants to rate how often a statement applies to them based on the following scale: 1 (rarely/never), 2 (occasionally), 3 (often), to 4 (always/almost always). The BIS–11 has been normed on a variety of sample populations, including college students, inpatient substance abusers, and prison inmates. Internal consistency for the scale was acceptable with Cronbach's α = .68.
Delay discounting
Developed by Kirby et al. (1999), the DDT provides a measure of the degree to which an individual shows preference for either small, immediate rewards or larger, delayed rewards, which may be stated as the rate at which the subjective value of deferred rewards decreases as a function of the delay until they are received. The DDT was provided in a paper-and-pencil format, consisting of a fixed set of 27 choices between smaller, immediate rewards and larger delayed rewards. For example, on the first trial, participants were asked “Would you prefer $54 today, or $55 in 117 days?” Delays included in this questionnaire ranged from 7 to 186 days. The presentation order of the delays was contrived so as not to correlate choice amounts, ratios, differences, delays, or discount rate implied by indifference to the two rewards. Previous research has shown that individual's discount curves are well described by the hyperbolic discount function (Mazur, 1987) V = A/(1 + kD), where V is the present value of the delayed reward A at delay D, and k is a free parameter that determines the discount rate. As k increases, the person discounts the future more steeply. Therefore, k can be thought of as an impulsiveness parameter, with higher values corresponding to higher levels of impulsiveness.
Risk-taking propensity
The BART (Lejuez et al., 2002) was used as a behavioral measure of risk-taking propensity. In this task, the computer screen displays a small simulated balloon accompanied by a balloon pump. Participants are directed to pump the simulated balloon to earn as much money as possible, taking into consideration that the balloon can pop at any time. When a balloon explodes, all money in the temporary bank is lost and the next uninflated balloon appears on the screen. At any point during each balloon trial, the participant can stop pumping the balloon by clicking a “Collect $$$” button, which transfers all money from a temporary bank to a permanent bank. After each balloon explosion or money collection, the participant's exposure to that balloon will end, and a new balloon will appear until a total of 20 balloons (i.e., trials) are completed. These 20 trials are comprised of different balloon types, all with the same probability of exploding. Participants are not given any detailed information about the probability of an explosion, but are told that at some point each balloon will explode and this explosion can occur as early as the first pump all the way up to the point at which the balloon expands as large as the computer screen. The primary dependent measure on the BART was the adjusted number of pumps across trials. An independent review by Harrison, Young, Butow, Salkeld, and Soloman (2005) of state of the art risk measurement strategies identified the BART has having excellent reliability (test–retest) and validity (including convergent and divergent).
Data Analytic Strategy
Analyses were conducted with the dichotomous variable of CU versus nonuse as the dependent variable. Primary analyses began with descriptive statistics for the entire sample across the dependent variable of event-level CU as well as each of the variables in the four domains of SAT: (1) environmental influences, (2) psychopathology and affect, (3) HIV-related attitudes and knowledge, and (4) self-regulatory skills/deficits. Next, we examined differences between those who used a condom versus those who did not at the last sexual encounter on all study variables using paired samples t tests and chi-square. Each variable found to significantly differ for the CU versus nonuse groups were included in a multivariate logistic regression analysis. All analyses used a two-tailed alpha of .05.
ResultsAs a preliminary analysis, we examined the descriptives of the last sexual encounter for the whole sample as shown in Table 1. For the dependent variable, condom nonuse, 66.0% reported not using a condom during this last sexual encounter. In terms of timing, 92.9% reported that the sexual intercourse occurred in the last year and 7.1% reported that the sexual encounter did not occur in the last year. Next, differences between those who used a condom versus not at last sexual encounter were examined; results are shown in Table 1. Of note, substance use frequencies were reported rather than dependence diagnoses given the more descriptive nature of continuous data, but it is notable that the same findings (nonsignificant relationships) were obtained when dependence diagnoses were used instead. Those who did and did not use a condom did not differ significantly on the following environmental influences: history of childhood trauma, whether the partner injected drugs with a needle, was HIV positive, or was having intercourse with other people, or whether the participant was drunk or high during the last sexual intercourse. Those who did and did not use a condom did not differ significantly on the following psychopathology and affect variables: presence of a diagnosis of major depression, any anxiety disorder, BPD, or ASPD, negative emotionality, frequency of crack, heroin, alcohol, and marijuana use. Last, they also did not differ on HIV-related knowledge, or on the following self-regulatory skills/deficits: trait nonplanning impulsivity, delay discounting (all ps > .06).
Differences in Condom Use at Last Sexual Intercourse
Those who used a condom were significantly older than those who did not use a condom, t(154) = –1.99, p < .05. The CU versus nonuse groups significantly differed on the type of relationship with the partner, χ2(2, N = 156) = 11.03, p < .01. A larger percentage of those who did not use a condom described their partner as regular. Those who did not use a condom scored higher on risk-taking propensity as indexed by the BART, t(154) = –1.98, p < .05. And finally, those who did not use a condom reported more negative attitudes towards condoms then those who did use a condom, t(154) = –3.02, p < .01.
Finally, we conducted a multivariate logistic regression analysis to determine the independent contribution of each variable related to condom nonuse at a univariate level. Each independent variable found to have a significant univariate relationship to condom nonuse was included in a multivariate logistic regression analysis. Results are reported in Table 2. Overall, the model correctly classified 71.8% of the sample. Age did not remain significant (p > .05). Partner type, risk-taking propensity, and condom attitudes each were significant. Specifically, respondents were significantly less likely to report unprotected sex with casual partners, odds ratio (OR) = 0.25, 95% confidence interval (CI) [0.10, 0.60], p = .002, and commercial partners, OR = 0.26, 95% CI [0.09, 0.75], p = .01, compared to regular partners. For risk-taking propensity, a one unit increase in level of BART was associated with an approximately one and half greater likelihood of not having used a condom at last sexual intercourse, OR = 1.61, 95% CI [1.09, 2.34], p = .02. Finally, a one unit increase in negative condom attitudes was associated with an approximately one and three quarter greater likelihood of not having used a condom at last sexual intercourse, OR = 1.78, 95% CI [1.11, 2.85], p = .02.
Multivariate Logistic Regression Predicting Condom Nonuse at Last Sexual Intercourse
DiscussionThis study sought to examine variables associated with CU measured via event-level assessment among a sample of urban substance users. Utilizing the SAT as a guiding framework, a variety of SAT derived predictors were examined including: (1) environmental influences (i.e., childhood trauma and characteristics of last sexual intercourse), (2) forms of psychopathology and affect (diagnoses of depression, anxiety, BPD, and ASPD, substance use frequencies, and negative emotionality), (3) HIV-related attitudes and knowledge (condom attitudes and HIV knowledge), and (4) self-regulatory skills/deficits (trait nonplanning impulsivity, delay discounting, and risk-taking propensity). Findings from the logistic regression indicated that regular partner type, higher levels of risk taking propensity, and negative condom attitudes all contributed uniquely to an increased likelihood of condom nonuse.
These results represent one of the first attempts to apply the SAT to understanding the factors influencing RSB in an urban, largely minority, substance abusing sample utilizing event-level methodology. The selected independent variables represent a range of theoretically relevant factors related to RSB according to SAT, and the final model supports several key aspects of SAT theory, specifically how it posits the various dimensions contribute to health risk behavior and in this case RSB. Specifically, the significance of contextual, cognitive, and self-regulatory factors suggests the utility of SAT. Although the findings leave questions open for future exploration, the resulting model for RSB is conceptually meaningful, as it suggests that having sex with a regular partner, having negative attitudes towards condoms, and being prone to take risks combine to confer a relationship with engagement in RSB (i.e., condom nonuse). In the following sections we discuss the findings in the context of each SAT domain.
Within the domain of environmental influences, the finding that having sex with a regular partner was related to condom nonuse is consistent with prior work with nondrug using samples (e.g., Macaluso et al., 2000), but deserves additional attention in considering its meaning in the context of this study sample (urban substance users). In many samples, the choice to have intercourse without a condom when one's partner is regular may be thought of as a fairly low-risk behavior. However, condom nonuse even with a regular partner may pose elevated risk in this particular population given the prevalence of injection drug use, risky sexual behavior, and/or commercial sex in urban, substance using environments. Future studies would benefit from collecting more objective information on the regular partner, as this description may vary greatly among respondents, as well as determining the specific routines and situations in which sex with regular partners does or does not increase risk for condom nonuse. This finding supports the proposal by van Empelen and colleagues (2003) to develop programs that specifically target safe sexual behaviors of drug users in the steady relationship context.
Also from the domain of environmental influences, an additional notable finding was that being drunk and/or high during the last incident of sexual intercourse was not significantly related to CU. Although a common perception exists that alcohol and other substances negatively interfere with CU, a variety of studies have questioned this notion and posited that one's likelihood to use a condom may remain unchanged regardless of intoxication state (e.g., Leigh, Vanslyke, Hoppe, Rainey, Morrison, & Gillmore, 2008; Weinhardt & Carey, 2000). In a chronic substance using sample, disentangling the effect of substance use on CU is challenging, given that the pharmacological effects of different types of drugs have been shown to have different impacts on RSB (Leigh, Ames, & Stacy, 2008). In a recent study, only amphetamines (smoked or injected) were consistently related to decreased CU; alcohol use was not related to decreased CU, and cocaine and marijuana were not significantly related to CU in either direction (Leigh et al., 2008a). Thus, our finding that being drunk or high at the last sexual encounter was not related to CU may not be surprising when considered in the context of the existing literature, particularly given that the two most prevalent drug dependencies in our sample were cocaine (54.6%) and alcohol (34.3%). Another potential factor that may have limited a significant relationship between intoxication and CU is the chronic nature of the sample's substance use. For the individuals in our sample, intoxication or activities aimed at future substance use often are pervasive aspects of daily routines, and therefore isolating acute pharmacological effects on CU may be complicated.
Considering psychopathology and affect, no significant relationships with CU emerged, which is consistent with a meta-analysis reporting no significant correlation between depressive symptomatology, anxiety, anger, and sexual risk taking (Crepaz & Marks, 2001). However, the absence of findings also may have been overly influenced by the timing of assessment. That is, diagnoses certainly vary over time and may have in fact had been relevant at the time of the last sexual encounter. As with more global measures of RSB, this type of timing issue is a weakness of event-level measurement across a variety of variables. Nevertheless, it is likely that this limitation is most impactful for variables assessing psychopathology as compared to more stable variables such as attitudes and self-regulatory skills/deficits, suggesting the importance of future work considering other strategies to adequately assess the impact of these variables. It will be necessary to move beyond cross-sectional data and to examine within-person associations (Kalichman & Weinhardt, 2001). For example, using an online diary, Mustanski (2007) found that lower levels of positive affect are related to increased sexual risk behaviors. The diary method offers the advantage of a short time period between behavior and recording allowing for detailed information about the co-occurrence of events and moods that fits well with the internal and environmental aspects of SAT.
Within the domain of HIV-related attitudes and knowledge, condom attitudes predicted event-level RSB. This finding supports previous studies that have demonstrated the relevance of condom attitudes in predicting RSB (e.g., Robertson & Levin, 1999; Zamboni, Crawford, & Williams, 2000), and as such, intervention efforts that target condom attitudes as a means to increase safe sex practices (e.g., Jemmott, Jemmott, Braverman, & Fong, 2005; Wingood et al., 2006) may be useful to extend to urban substance using populations that face heightened HIV risk, with a particular focus on those with a propensity to take risks. The lack of significant relationship with HIV knowledge is consistent with prior research demonstrating that HIV/AIDS knowledge and perceived risk have little predictive value for safe sexual behavior (c.f. van Empelen et al., 2003). Findings for condom attitudes despite the absence of findings for HIV knowledge suggests that it is not necessarily what is known about the risks and consequences associated with HIV, but one's internal representation (attitudes in this case) resulting from knowledge and other external variables.
Finally, risk-taking propensity, as indexed by performance on the BART (a tendency that may limit self-regulatory resources) was related to condom nonuse during one's last sexual encounter. This is consistent with the Lejuez et al. (2004) study that found risk-taking propensity, as indexed by the BART, to be significant predictor of RSB, measured globally, above and beyond a host of variables. These findings serve as an extension of previously published reports and suggest the value of adding behavioral assessments such as the BART in understanding RSB, given that they are not compromised by limitations in individuals' ability to recognize and report on their own behavioral tendencies (Leigh & Stall, 1993; Tortu et al., 2000). Interpreting the factors underlying this relationship is difficult as the current study fails to rule out the possibility that both RSB as well as risk-taking propensity are the consequences of immersion in a substance using lifestyle as well as the direct consequence of neurotoxic illicit drugs, such as crack/cocaine (Booth, Kwiatkowski, & Chitwood, 2000; Hoffman et al., 2000; Ross et al., 2002). Further, this study fails to explain why trait nonplanning and delay discounting, both variables within the disinhibition umbrella, were not related to condom use as previous work among substance using samples has found impulsivity, measured with the Eysenck I7 Questionnaire (Eysenck, Pearson, Easting, & Allsopp, 1985), to be a statistically significant predictor of sexual risk after adjusting for the effects of demographic variables and substance use frequency (Hayaki et al., 2006). Future studies should be designed to systematically tease apart the potential contributions of dispositional factors from long-term negative effects of drug use in trying to better understand the origins of RSB. In addition, future work should also carefully consider the measurement of disinhibition as it is considered to be a multidimensional construct. It may be that certain aspects of disinhibition (e.g., risk-taking propensity) may be related to RSB whereas others are not. It is necessary to parse apart the differential findings with various measures of disinhibition, particularly the lack of consistency with self-report and behavioral tasks and the lack of significant correlation, as was found in this study, between measures that tap disinhibition.
The current study has a number of strengths, including the use of an at-risk, underserved sample of urban, primarily minority substance users, the application of the SAT framework, and the application of event-level measurement and behavioral assessment of risk-taking propensity to this population. However, several limitations should also be considered in interpreting the results. As noted above, there were multiple confounds associated with timing. It is unclear the extent to which measurement of study variables at our assessment accurately represent the manifestation of that variable at the most recent sexual experience, with only the contextual variables affecting condom use being variables in our battery directly tied to the most recent sexual encounter (i.e., to what extent are current condom attitudes at the assessment similar to those at the most recent sexual encounter; could not using a condom impact one's condom attitudes). Also presenting interpretative challenges, the current study did not assess the precise timing of the last sexual encounter (just whether the sexual encounter occurred in the previous year), thereby preventing statistical control for important factors (e.g., how long ago the last sexual encounter took place) to assess the temporal relationship among the predictors and our outcome measure.
Although the event-level measurement may help with overreporting and rounding and allows for an examination of situational variables, it is also important to acknowledge its limitations. There is the potential that the event reported may not be representative of the participant's overall behavior, and therefore cannot provide information on cumulative risk. The single event does not allow within person comparisons on behavior with regard to the environmental variables assessed, such as partner type or substance use, leaving open questions about whether the individual participant's behavior varies with regard to these factors. Further, the event-level report is still retrospective. Future work examining person-level predictors (e.g., risk taking propensity, condom attitudes) in addition to event characteristics (e.g., intoxication state, partner type) would benefit from repeated sampling with techniques as a daily diary to determine which variables are consistently associated with discrete behavior. Further, there is some evidence that recalls up to 90 days are reliable (e.g., Ajzen & Fishbein, 2004; Jaccard, McDonald, Wan, Dittus, & Quinlan, 2002); thus, in future work it may be best to limit event-level measurement to sexual encounters within the last 90 days or to conduct a prospective study that attempts to link current variables with sexual behavior.
Another limitation is the failure to assess sexual orientation and/or gender of sexual partner, a necessary target for future work. A final limitation is the size of the sample. Although sample size is acceptable for main findings, a number of interactions were examined (see footnote) that were not found to be significant. Although power was indeed limited for these analyses, it is notable that the odds ratios and confidence ratios indicate that these were very modest. Nevertheless, future studies would benefit form larger samples to further evaluate the utility of the SAT model, including examination of potential interaction effects.
The current data support a model framed in SAT in which having sex with a regular partner, having negative attitudes towards condoms, and being prone to take risks combine to confer a relationship with engagement in RSB (i.e., condom nonuse). Despite limitations and the questions raised by our findings, the current results add to the growing body of literature evaluating and predicting RSB within urban substance using populations and begins to lay the groundwork for future investigations that examine event-level RSB in the context of more elaborate environmental and self-regulatory predictors. By identifying personal and environmental self-regulatory resources that directly affect social interactions, problem solving, and self-efficacy, SAT applied to RSB has the potential to identity interventions that can be used to enhance health by altering self-goals, strategies, and environments. From a public health standpoint, identifying factors that influence HIV risk behaviors will continue to be critical, particularly as evidence grows suggesting that urban, minority substance users are at high risk for HIV infection.
Footnotes 1 We also conducted several follow-up analyses. First, we examined if the relationship of condom use with risk taking propensity as well as condom attitudes varied by partner type. The interactions of partner type and risk taking propensity, OR = 1.00, 95% CI [0.97, 1.04], p = .94, and partner type and condom attitudes, OR = 1.36, 95% CI [0.71, 2.60], p = .36 were not significant. Next, although there was not a significant association between being drunk or high and condom use, an interaction between partner type and being drink or high was examined based on prior work demonstrating a significant interaction. The interaction of being drunk or high with partner type was not significant, OR = 2.05, 95% CI [0.66, 6.33], p = .21. Finally we examined gender interactions with the variables significantly related to condom use based on the univariate findings as shown in Table 1. There was not a significant interaction of gender with partner type, OR = 1.05, 95% CI [0.28, 3.93], p = .94, gender with condom attitudes, OR = 1.08, 95% CI [0.42, 2.75], p = .87, or gender with risk taking propensity, OR = 1.02, 95% CI [0.97, 1.07], p = .43.
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Submitted: April 14, 2009 Revised: October 26, 2009 Accepted: December 11, 2009
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Source: Psychology of Addictive Behaviors. Vol. 24. (2), Jun, 2010 pp. 311-321)
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Record: 7- Assessing motivational interviewing integrity for group interventions with adolescents. D'Amico, Elizabeth J.; Osilla, Karen C.; Miles, Jeremy N. V.; Ewing, Brett; Sullivan, Kristen; Katz, Kristin; Hunter, Sarah B.; Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012 pp. 994-1000. Publisher: American Psychological Association; [Journal Article] Abstract: The group format is commonly used in alcohol and other drug (AOD) adolescent treatment settings, but little research exists on the use of motivational interviewing (MI) in groups. Further, little work has assessed the integrity of MI delivered in group settings. This study describes an approach to evaluate MI integrity using data from a group MI intervention for at-risk youth. Using the Motivational Interviewing Treatment Integrity (MITI) scale, version 3.1, we coded 140 group sessions led by 3 different facilitators. Four trained coders assessed the group sessions. Agreement between raters was evaluated using a method based on limits of agreement, and key decisions used to monitor and calculate group MI integrity are discussed. Results indicated that there was adequate agreement between raters; we also found differences on use of MI between the MI-intervention group and a usual-care group on MI global ratings and behavioral counts. This study demonstrates that it is possible to determine whether group MI is implemented with integrity in the group setting and that MI in this setting is different from what takes place in usual care. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Assessing Motivational Interviewing Integrity for Group Interventions With Adolescents
By: Elizabeth J. D'Amico
RAND Corporation, Santa Monica, California;
Karen C. Osilla
RAND Corporation, Santa Monica, California
Jeremy N. V. Miles
RAND Corporation, Santa Monica, California
Brett Ewing
RAND Corporation, Santa Monica, California
Kristen Sullivan
Department of Clinical, Counseling, and School Psychology, University of California, Santa Barbara
Kristin Katz
Department of Clinical, Counseling, and School Psychology, University of California, Santa Barbara
Sarah B. Hunter
RAND Corporation, Santa Monica, California
Acknowledgement: We thank the Council on Alcoholism and Drug Abuse for their support of this project. We would also like to thank Cally Sprague and Susana Lopez for their help on the study. The current study was funded by a grant from the National Institute of Drug Abuse (R01DA019938) to Elizabeth D'Amico.
The group format is commonly used to treat adolescents who use alcohol and other drugs (AOD; Kaminer, 2005; Vaughn & Howard, 2004), even though little is known about the distinguishing factors of effective and ineffective group interventions (Engle, Macgowan, Wagner, & Amrhein, 2010). Recent work with at-risk adolescents indicates that motivational interviewing (MI) interventions (Miller & Rollnick, 2002; Rollnick, Miller, & Butler, 2008) can be successful, as MI offers a collaborative, nonjudgmental, and nonconfrontational approach (Naar-King & Suarez, 2010). Although studies of youth receiving individual MI have shown effectiveness (Baer, Garrett, Beadnell, Wells, & Peterson, 2007; D'Amico, Miles, Stern, & Meredith, 2008; Monti et al., 2007; Spirito et al., 2004), there are only four published group MI studies (Bailey, Baker, Webster, & Lewin, 2004; D'Amico, Osilla, & Hunter, 2010; Engle et al., 2010; Schmiege, Broaddus, Levin, & Bryan, 2009). Furthermore, MI treatment integrity has been mainly conducted with individual interventions (Baer et al., 2004; Martino, Ball, Nich, Frankforter, & Carroll, 2008; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005; Vader, Walters, Prabhu, Houck, & Field, 2010). Only one study, to date, has evaluated MI integrity with adolescent group sessions (Engle et al., 2010) using measures of MI competence and adherence, such as the Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, Miller, & Ernst, 2010).
Treatment integrity is important for ensuring that a therapy is delivered as intended by the treatment developers (Beidas & Kendall, 2010; Godley, Garner, Smith, Meyers, & Godley, 2011; Rakovshik & McManus, 2010). The MITI scale (Moyers et al., 2010) is a widely used measure of MI competence and adherence. Engle et al. (2010) used the MITI scale to examine the group process and found that group facilitator empathy was associated with more positive commitment language (e.g., “I'm quitting for the summer”), which was then associated with reduced AOD use (Engle et al., 2010). Although this study was pioneering, additional studies are needed, as only 19 sessions were coded, only one of the MITI global ratings (empathy) was assessed, and there was no comparison group. Thus, to move the field forward, more detailed treatment-integrity analyses (i.e., more group sessions and a comparison group) are needed so that we can assess how MI groups differ from other adolescent groups.
Finally, in order for treatment integrity to be successfully measured, alternative measurements to intraclass correlation (ICC) are needed to assess interrater agreement (e.g., Shrout & Fleiss, 1979). Calculating the ICC is not always feasible, as there may be different sets of raters for each session or there may be missing data (e.g., one session might be coded by all raters but another session may not). Thus, other approaches that compensate for these limitations must be utilized in order for studies to quantify interrater agreement.
The current study addresses these gaps by using the global and behavioral counts on the MITI to (a) assess agreement between raters, (b) calculate interrater agreement using two alternative methods to the ICC, and (c) evaluate the use of MI between a group led by facilitators trained and supervised in MI compared with a group with a self-help and Alcoholics Anonymous focus. This is an important first step in understanding whether the MITI is feasible for use in group settings and for determining MI integrity in this setting. We hypothesized that coders would reliably assess MI behaviors and that trained and supervised MI facilitators would deliver more MI-consistent behavior than the facilitator in the usual care (non-MI) group sessions.
Method Study Overview
This study was conducted as part of a randomized clinical trial (D'Amico, Hunter, et al., 2010). Procedures were approved by the research institution's internal review board. We collaborated with the Council on Alcoholism and Drug Abuse, a nonprofit organization in Santa Barbara, California, that operates a Teen Court (TC) for first-time offending youth. Based on earlier assessments, adolescents who do not need intensive treatment can participate in TC in lieu of formal juvenile justice processing. As part of TC, youth with a first time AOD offense are required to attend six AOD awareness groups, along with other sanctions (e.g., community service). Adolescents who successfully complete their sentence have the offense expunged from their record.
The AOD awareness groups occur weekly. Entrance to the groups is based on rolling admission, as each session can stand alone without a teen having to complete a previous session—that is, attending Session 2 does not require information from Session 1. Teens enter the sessions based on their time of referral to the program. Teens were randomized to either a usual care (UC) control condition or the experimental MI group intervention called Free Talk (FT). Teens were automatically assigned to attend the UC groups if they or their parent refused participation. Refusals (10%) were mostly due to lacking time or transportation to complete a baseline survey before their first group session. There were no demographic or offense differences between refusers and participants. See Table 1 for FT participant characteristics.
Characteristics of the Adolescents in the Free Talk and Usual Care Groups (N = 102)
The Current Study
This study focuses on the facilitator's behavior and whether this behavior can be reliably coded during adolescent group sessions. We provide detailed information on monitoring of a group MI intervention with at-risk adolescents (n = 102) across a large number of group sessions (n = 140) and compare these MI adolescent groups with usual care groups using the MITI.
Intervention Condition: Free Talk Groups
Free Talk was developed as part of a Stage I study (Rounsaville, Carroll, & Onken, 2001) where each group session was iteratively tested to determine feasibility and acceptability of intervention content (D'Amico, Osilla, et al., 2010). The facilitator manual for FT included a protocol for each session and utilized a MI approach. At the beginning of each session, the facilitator discussed the group guidelines (e.g., confidentiality, respect for others in the group). These were provided in a MI-consistent way (e.g., ensuring members were respectful of one another). FT is a manualized intervention that provides some pyschoeducation and focuses on encouraging change talk (see D'Amico, Osilla, et al., 2010, for content). For example, throughout all sessions, the focus is on providing reflections, asking open -ended questions to increase collaboration, affirming and supporting the adolescents to increase support and autonomy, and increasing change talk in the group by being aware of the DARN-C (Desire, Ability, Reason, Need, and Commitment; Amrhein, 2009). We also used tools to promote behavioral change, such as the decisional-balance activity and willingness and confidence rulers (Ingersoll, Wagner, & Gharib, 2006; Miller & Rollnick, 2002). Sessions lasted 55 min and mean group size was 4.5 adolescents (SD = 1.98).
Free Talk Training and Integrity Monitoring
The FT sessions were led by one of three facilitators (all female and White) who were psychology doctoral students with prior at-risk teen work experience. They received 40 hours of MI and FT training delivered by E.D. and K.O., clinical psychologists affiliated with the Motivational Interviewing Network of Trainers (MINT).
Facilitators were instructed to use MI to best fit a group format while still attending to individuals (D'Amico, Feldstein Ewing, et al., 2010; Velasquez, Stephens, & Ingersoll, 2006). For example, reflections were often stated to address the group process (e.g., “Many of you have been making positive changes in your lives”), and yet facilitators also responded to the varying individual experiences and needs (e.g., rolling with the resistance of one youth while trying to actively maintain the commitment language of another). In addition, facilitators monitored participant feedback and redirected negative or unhelpful comments to create a safe place for participation and mutual respect.
All FT groups were digitally audio recorded. MINT trainers reviewed all recordings and provided 1-hr weekly supervision to facilitators. The MITI was used to monitor performance (i.e., provide feedback during supervision).
Control Condition: Usual Care Groups
The UC condition was led by one facilitator who was male and Hispanic. The curriculum followed an abstinence-based Alcoholics Anonymous approach. Topics included group check-in, discussion of personal triggers, consequences of AOD use, educational videos, discussion of personal experiences with AOD use, and myths about AOD use. Like the FT groups, each session lasted about 55 min.
Coding
Overview
One hundred and 40 sessions (70 FT and 70 UC sessions) were coded by four psychology doctoral students. Per the MITI protocol, a randomly selected 20-minute segment was coded for both FT and UC sessions (Moyers et al., 2010). FT sessions were coded using digital audio recordings and UC sessions were coded from live observation, as not all teens attending these groups were study participants, so it was not possible to record these sessions. Raters were trained to not disrupt the UC groups by arriving early, sitting off to the side, not interacting with teens, and being discreet with coding materials.
MITI
The MITI is a widely used instrument for coding competency and adherence to MI and has been used in numerous studies to assess MI integrity (Jensen et al., 2011; Moyers et al., 2005; Tollison et al., 2008; Turrisi et al., 2009). It was specifically designed for clinical trials and is a reliable and valid tool (Madson & Campbell, 2006; Moyers et al., 2005). Integrity is measured through the use of global scores (e.g., amount of collaboration) and behavioral counts (e.g., number of reflections; Moyers et al., 2010). The MITI is used to assess the therapist's behavior during a session and focuses solely on the therapist's behavior; thus, the MITI can easily be utilized to assess a group facilitator's behavior (e.g., Engle et al., 2010). MITI 3.1 has five global scales (collaboration, empathy, evocation, autonomy/support, and direction) that are scored on a scale from 1 (low) to 5 (high). MITI competency is defined as an average of 4 on the global ratings (Moyers et al., 2010). As noted in the MITI manual, collaboration occurs when there is little power differential, there is agreement on goals, and clients are encouraged to share the talking. Empathy occurs when the facilitator expresses client understanding and attempts to understand client point of view. Evocation occurs when the facilitator encourages clients to brainstorm reasons and ideas for how to change. Autonomy/support occurs when the facilitator emphasizes and supports client's personal choice. Direction occurs when the facilitator exerts influence on the session and generally does not miss opportunities to direct client toward the target behavior or referral question (Moyers et al., 2010). In the group setting, the facilitator responds to group members, thus, group members influence the facilitator's behavior. For example, if a facilitator is not collaborative and does not ask open-ended questions, then there will be less participation and less sharing of the talking. Facilitator responsiveness to group member behavior is captured by the MITI global scores.
The rater also counts specific behaviors that occur during each coded segment, including open-ended questions, closed-ended questions, MI-adherent (e.g., “If it's ok with you, I'd like to share some information with you”) and nonadherent statements (e.g., “You need to stop drinking”), and simple (e.g., “some of you are ready to make changes”) and complex reflections (e.g., “some of you are hoping that by making changes, things will improve in your lives”). Whereas global scores have a range limit (1 to 5), behavioral counts have no upper end on the scale; thus, these scores can vary by session to a greater degree.
Coding Training
Four raters received about 40 hours of training, which included a half-day MITI training and practice coding assignments (http://casaa.unm.edu/codinginst.html). Similar to other studies (Moyers et al., 2005; Tollison et al., 2008), raters met weekly to discuss discrepancies. Interrater agreement was stable over time. Raters coded both UC and FT group sessions.
Interrater Agreement for MITI
All 140 sessions were coded by at least one rater, with 48 (34%) sessions coded by two raters (19 UC and 29 FT) and 25 (18%) FT sessions coded by three raters. We did not have three raters code UC sessions, given that these groups were coded “live” and this would have been disruptive. Raters were not the same two people each time, as they were assigned to code sessions randomly. Thus, the ICC could not be calculated, as different raters coded different sessions and ICC is not defined in the presence of missing data. In addition, using the ICC in this case would not be an accurate comparison, as it is like reporting correlations among items rather than a coefficient alpha. Interpretation of these coefficients would also be difficult because many of them have small sample sizes and hence we do not have a sense of the sampling distribution of the statistic.
We therefore used two different methods to quantify agreement between raters. Our first method was distribution-free: We calculated the difference between each rater's rating of a session and the mean rating for that session. To see how close the majority of ratings were to the mean, we ordered these differences from the smallest to the largest. We then took the 95th percentile (e.g., if there were 100 differences, we took the 95th rating, referred to as D95 in Table 2). Thus, the D95 value indicates that 95% of differences between each rater and the mean rating are smaller than this value. The D95 is more appropriate for dichotomous variables, as it makes no distributional assumptions.
MITI Measures of Agreement Between Raters and Mean Scores by Rater Across Both the Free Talk and Usual Care Groups
In our second method, we used a normal distribution approach of calculating the within-session standard deviation (WSSD) to provide an estimate of the difference between raters (Altman & Bland, 1983; Bland & Altman, 1986, 2007). The WSSD is closely related to the average of the difference between raters, ∑(x−
/N, where
is the mean for the session and x represents the set of ratings. The WSSD is given by the formula for the standard deviation (∑(x−
)2/(N−1))0.5. Because the WSSD squares the differences between each rating and the mean, larger deviations between raters lead to proportionally larger estimates. If we assume differences are normally distributed, we expect 95% of ratings to lie within about two standard deviations of that mean. For interpretation purposes, we therefore report 2*WSSD, which allows us to compare the two methods.
These two methods make different assumptions about the distribution of ratings. The D95 method does not make distributional assumptions, but it does not use all of the information that is available regarding the values. In contrast, the WSSD method uses all the information and assumes that data are normally distributed and measured on a continuous scale. Using two methods allowed us to examine interrater agreement sensitivity and compare the results of the two methods.
These agreement measures have two advantages over the ICC. First, the ICC is a ratio of between-group variance to total variance. The ICC depends not only on the extent of the agreement between raters but also on the variance of the measures. Thus, the ICC could be lower or higher, depending on the level of variance, even if the rater agreement level was the same in both cases. Second, the ICC presents agreement on a scale from 0.0 (no agreement) to 1.0 (perfect agreement). Although the scale allows for a comparison across studies, the ICC has little interpretive value of how actual scores differ between raters, as the units of the ICC are not the same as the units on the scale. This variation is very important for scales like the MITI that have items ranging from 1 to 5 points because it provides data on how to recalibrate coding to ensure that they match.
Results Interrater Agreement
We calculated the limits of agreement between the raters for each of the MITI dimensions using the D95 and 2*WSSD methods. Table 2 shows ratings for both groups combined across all four raters. For MITI global ratings using the D95 method, raters were within 0.5 points of the mean for the session 95% of the time. Using the 2*WSSD method, the level of agreement was similar, with expected 95% limits ranging from 0.50 (autonomy/support and empathy) to 0.62 (evocation).
For the behavioral counts, there was no upper limit; thus, we expected to see larger differences. Both approaches yielded comparable limits of agreement. The D95 limits ranged from 3.56 (complex reflections) to 5.56 (MI-adherent and closed questions). The 2*WSSD limits ranged from 3.26 (complex reflections) to 5.54 (closed questions). As one would expect, MI-nonadherent counts had lower D95 (0.50) and 2*WSSD (0.36). There are no published MITI benchmarks for what consists of good agreement. For this study, we consider within 1 point on the MITI global scores and within 6 points on the behavioral counts as good levels of agreement.
MI Differences by Group
Table 3 shows differences between the means of all raters across groups. Overall, both global and behavioral count scores were higher for the FT group compared with the UC group, indicating greater MI integrity in the FT group sessions.
MITI Means and Standard Deviations Across Raters by Group
DiscussionThis study is one of the first to assess MI integrity using data from an adolescent group intervention. First, this study found that group MI can be efficiently and reliably assessed using alternatives to ICC. We used two innovative methods to address agreement and both found comparable results. These methods are more useful than the ICC because they can be calculated when using multiple coders and are better able to quantify the difference between multiple raters, providing us with a more direct interpretation of the level of agreement. For example, a D95 or 2*WSSD score of 2 tells us that 95% of raters are expected to be within 2 points of the mean of all raters. A clinical judgment must then be made to determine whether the raters are sufficiently close to agreement, which depends upon on the units of the original measure. A difference of 2 points on global ratings is very large compared with behavioral counts. Thus, our findings of less than .62 difference between a rater and the mean score on the global ratings and 5.56 on the behavioral counts suggest that the raters were in fairly close agreement. Using these indices of interrater agreement could be useful in clinical practice, as it bridges the gap between research and practice by allowing for real-world situations such as multiple raters or supervisors.
We found that it is feasible to train facilitators to conduct MI in an adolescent group setting and that MI can be measured and implemented with integrity. Overall, global scores for the FT group were in the competent range and behavioral counts for MI behaviors were high. We expected this, given the intensive training and supervision; MI fidelity might not be as high with less supervision. In addition, future research could measure integrity across more facilitators.
We found large differences between the FT and UC groups on collaboration, empathy, evocation, and autonomy/support, suggesting that FT groups were more likely to encourage power sharing during the session, had repeated efforts to gain understanding of teens' viewpoints, had more acceptance of teens' reasons for change, and were more likely to emphasize support of client autonomy. The behavioral count data showed a similar pattern with more simple and complex reflections and open-ended questions in FT groups. More MI-nonadherent behavior was observed in the UC groups, such as confronting and advising without permission compared with the FT groups. These findings are not surprising, given that the UC group facilitator did not receive MI training or supervision but rather followed an Alcoholics Anonymous group treatment approach. Our study findings suggest that, similar to MI research with individuals (Moyers et al., 2005), group MI could make the overall group process more collaborative and therefore more effective, and this may reduce the likelihood of iatrogenic effects that are sometimes seen in groups of at-risk youth (e.g., Dishion, McCord, & Poulin, 1999; Dodge, Dishion, & Lansford, 2006). For example, as shown with individual work (Magill, Apodaca, Barnett, & Monti, 2010; Moyers, Martin, Houck, Christopher, & Tonigan, 2009), the increased evocation and autonomy/support may provide adolescents with a safe place to explore reasons for change without fear of being forced to change; the guiding and empathic style of MI may then help move participants toward positive behavior change.
This study had limitations. First, raters were not blind to study condition. Similar to other MI brief interventions (e.g., Hettema, Steele, & Miller, 2005), FT sessions followed a protocol and could be easily distinguished from other types of sessions. Second, our UC control condition had to be coded by live observation; thus, it is possible that some information was missed. However, raters coded both UC and FT with one pass, so we feel confident that raters captured most of the behaviors during the UC sessions. Moreover, our results indicated that raters were consistent in their ratings across both groups and had high agreement, suggesting that they were not biased. Third, no prior research has employed these statistical methods to address interrater agreement for the MITI; thus, no consensus exists regarding how much difference among raters might be “acceptable.” We do not consider this a problem, as cutoff values may vary across studies and may not be quantifiable, and the use of such values could limit researchers from interpreting results objectively. Thus, it is important to use clinical judgment when evaluating the level of agreement with these methods. Finally, we did not assess group cohesion or engagement; more work is needed in this area.
In sum, this study takes an important first step in documenting group MI with at-risk youth. This study is among the first to provide within-session standard deviations for all global and behavioral counts that comprise the MITI scoring system. Thus, this work can provide a potential resource to practitioners and researchers who do group work and are interested in using MI and measuring MI integrity in this setting. The next step in this work is to examine intervention efficacy; we are currently conducting a randomized trial to assess the short-term effects of this intervention on AOD use among at-risk teens.
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Submitted: August 1, 2011 Revised: November 8, 2011 Accepted: January 9, 2012
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 994-1000)
Accession Number: 2012-13792-001
Digital Object Identifier: 10.1037/a0027987
Record: 8- Association of solitary binge drinking and suicidal behavior among emerging adult college students. Gonzalez, Vivian M.; Psychology of Addictive Behaviors, Vol 26(3), Sep, 2012 pp. 609-614. Publisher: American Psychological Association; [Journal Article] Abstract: [Correction Notice: An Erratum for this article was reported in Vol 26(3) of Psychology of Addictive Behaviors (see record 2012-13892-001). In the article, there is an error in the introductory paragraph. The number of students who had seriously considered attempting suicide in the Barrios, Everett, Simon, & Brener (2000) study should have been reported as 11.4%, not 1.4%. Additionally, in the Participants section, data for the study were collected from March 2009 to September 2010, not March 2009 to January 2010 as reported.] Emerging adult college students who binge drink in solitary contexts (i.e., while alone) experience greater depression and suicidal ideation than do students who only binge drink in social contexts, suggesting that they may be at greater risk for suicidal behavior. This study examined the association of a previous suicide attempt, one of the best predictors of future suicide attempts and suicide, and severity of recent suicidal ideation with drinking in solitary and social contexts. Participants were binge drinking, emerging adult (18- to 25-year-old) college students (N = 182) drawn from two studies of college drinkers. A logistic regression analysis revealed that both suicide attempt history and severity of suicidal ideation were significantly associated with a greater likelihood of being a solitary binge drinker as opposed to only a social binge drinker. Students with a previous suicide attempt were nearly four times more likely to be solitary binge drinkers. Multiple regression analyses revealed that suicide attempt history was significantly associated with greater frequency and quantity of drinking in solitary, but not social contexts. Suicidal ideation was significantly associated with drinks per solitary drinking day, but not frequency of solitary drinking once suicide attempt history was accounted for. Given the associations found between solitary binge drinking and a history of suicide attempts, as well as greater severity of recent suicidal ideation, it appears that these students are in need of suicide prevention efforts, including treatment efforts aimed at reducing binge drinking. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Association of Solitary Binge Drinking and Suicidal Behavior Among Emerging Adult College Students
By: Vivian M. Gonzalez
University of Alaska Anchorage;
Acknowledgement: See page 620 for a correction to this article.
This research was supported by funds provided through the University of Alaska Anchorage Chancellor's Fund and a National Institute on Alcohol Abuse and Alcoholism grant (R21AA018135) to Vivian M. Gonzalez.
College students have high rates of “binge” drinking, defined as four or more drinks for women or five or more drinks for men in one sitting or on one occasion (Wechsler, Davenport, Dowdall, & Moeykens, 1994), and alcohol problems (Knight et al., 2002; Slutske, 2005; Timberlake et al., 2007). American college students also engage in high rates of suicidal ideation and behavior (Brener, Hassan, & Barrios, 1999; Furr, Westefeld, McConnell, & Jenkins, 2001), with 1.7% reporting a suicide attempt and 1.4% reporting seriously having considered attempting suicide during the preceding year (Barrios, Everett, Simon, & Brener, 2000).
There is a well-established link between alcohol use and disorders and suicide attempts and completions (Borges, Walters, & Kessler, 2000; Cherpitel, Borges, & Wilcox, 2004; Hufford, 2001; Wilcox, Conner, & Caine, 2004). Among college students, individuals with suicidal ideation are more likely to binge drink, and alcohol problems in this population are associated with increased rates of suicidal ideation and attempts (Brener et al., 1999; Gonzalez, Bradizza, & Collins, 2009; Levy & Deykin, 1989; Stephenson, Pena-Shaff, & Quirk, 2006).
Research suggests that the drinking context plays an important role in the association between alcohol use and negative affect, including suicidality. In college students, drinking tends to occur for social reasons (Kuntsche, Knibbe, Gmel, & Engels, 2005; LaBrie, Hummer, & Pedersen, 2007; Stewart, Zeitlin, & Samoluk, 1996) and in social contexts (Christiansen, Vik, & Jarchow, 2002; Mohr et al., 2001). However, drinking in response to negative experiences and affect is associated with drinking in solitary, as opposed to social, contexts (Mohr et al., 2001). Students who engage in solitary binge drinking (i.e., while alone or when no one else is drinking) have more severe depression (Christiansen et al., 2002) and suicidal ideation (Gonzalez, Collins, & Bradizza, 2009) than students who only binge drink in social contexts.
Depression and suicidal ideation are associated with suicide and suicide attempts (Brown, Beck, Steer, & Grisham, 2000; Kuo, Gallo, & Tien, 2001; Kuo, Gallo, & Eaton, 2004). Given the association of solitary binge drinking with these variables, solitary binge drinkers may be at higher risk for suicidal behavior than are students who binge drink only in social contexts. However, no study to date has examined the association between solitary drinking and previous suicide attempts, one of the best predictors of risk for suicide and suicide attempts (Borges, Angst, Nock, Ruscio, & Kessler, 2008; Harris & Barraclough, 1997; Oquendo et al., 2004).
This study examined whether individuals with a suicide attempt history were more likely to be solitary as opposed to social binge drinkers. The associations of suicide attempt history and suicidal ideation with frequency and quantity of drinking in social and solitary contexts also were explored to further examine the association of drinking context and suicidality.
Method Participants
Participants for the current study were drawn from two studies of emerging adult college drinkers (Gonzalez & Skewes, 2011; Gonzalez, Reynolds, & Skewes, 2011). For both studies, eligibility criteria included (a) being a full- or part-time university student and (b) being between the ages of 18 and 25 years old. Each study had additional selection criteria. In Study 1, participants had to have at least one solitary or social binge drinking episode per month during a typical month in the past year. In Study 2, participants had to have consumed at least four standard drinks in the past month and report either current sadness or loss of pleasure. The sadness and loss of pleasure items were adapted from the Beck Depression Inventory-II (Beck, Steer, & Brown, 1996), with items needing to be endorsed as at least a 1 (i.e., not enjoying things as much or feeling sad much of the time) to meet the inclusionary criterion. Although Study 2 included the sadness or loss of pleasure criterion, combining these samples was advantageous for the current study, as depression has been found to be associated with the key variables of interest in this study: solitary binge drinking and suicidality. Data in these studies were collected from March 2009 to January 2010. For the current study, individuals were not included if they did not binge drink during a typical month in the past year (n = 31), gave out-of-range and/or highly inconsistent and illogical responses across alcohol use items (n = 25), or were missing drinking data (n = 2).
Participants were 182 binge drinking, emerging adult (18- to 25-year-old) female (72.5%, n = 132) and male (27.5%, n = 50) college students attending a large, open-enrollment university in the Northwest. The average age was 21.1 years old (SD = 1.9). The sample was 74.7% White, 10.4% Alaska Native or American Indian (including mixed heritage), 4.4% Latino, 4.4% multiethnic, 2.2% Asian American, 1.6% Pacific Islander, and 1.1% African American. The majority of participants were full-time students (88.5%) and single (95.6%). With regard to living arrangements, 23.8% lived in their parents' home, 53.5% lived off campus (not in a parent's home), and 22.7% lived on campus. The sample was 19.8% freshman, 24.2% sophomore, 24.2% junior, 23.6% senior, 6.0% graduate students, and 2.2% nondegree-seeking.
Procedures
In both Studies 1 and 2, participants were recruited via flyers posted on campus and e-mails directed at 18- to 25-year-old students via their student e-mail accounts. Flyer and e-mail solicitations directed potential participants to a webpage that described the respective study in general terms (e.g., “a study of college student lifestyle and mood”) and included a questionnaire with items that screened for study eligibility embedded among distractor items. Those who met eligibility criteria for each respective study were scheduled for an in-person data collection session where study materials were presented in random order using MediaLab version 2006 software (Jarvis, 2006) on laptop computers. Participants were compensated for their time with a gift card ($20 for Study 1 and $30 for Study 2, given the longer protocol) to a supermarket/gas station or coffee-shop chain. After data collection, all participants were individually debriefed and given referral information for counseling services, as well as suicide/crisis-hotline phone numbers. The study protocols were approved by the institutional review board of the university.
Measures
Alcohol consumption
Solitary and social alcohol consumption were measured using items modified from the National Institute on Alcohol Abuse and Alcoholism's (NIAAA) alcohol consumption question set (NIAAA, 2003). Three separate items for social (defined as “with other people who were drinking”) and solitary (defined as “while alone or no one else was drinking”) contexts asked the following for a typical month in the past year: drinking days per month, number of standard drinks consumed on a typical drinking day, and the number of days on which binge drinking (i.e., four or more for women, or five or more for men, standard drinks on one occasion or sitting) occurred. Participants were provided with a handout that defined a standard drink (e.g., 12 oz. of beer, 5 oz. of wine, 8 to 9 oz. of malt liquor, or 1.5 oz. of 80-proof liquor).
Suicide attempt history
The Suicidal Behaviors Questionnaire—Revised (SBQ-R; Osman et al., 2001) is a 4-item self-report measure of suicidal behavior and ideation. The first item assesses lifetime suicidal ideation and behavior (“Have you ever thought about or attempted to kill yourself?”), with mutually exclusive response options ranging from no history of suicidal ideation or behavior (never) to history of a suicide attempt (I have attempted to kill myself). This item was used in the current study to categorize participants as having or not having a suicide attempt history. In young adult samples, the SBQ-R demonstrates high 2-week test–retest reliability (r = .95) and good convergent validity (Cotton, Peters, & Range, 1995).
Suicidal ideation
The Adult Suicidal Ideation Questionnaire (ASIQ; Reynolds, 1991a) is a 25-item self-report measure of suicidal thoughts and behavior experienced during the past month. Items range from general wishes that one were dead to thoughts of planning a suicide attempt and are rated on a 7-point scale (0 = never had the thought to 6 = almost every day). The ASIQ demonstrates high 1-week test–retest reliability (r = .86) and good convergent validity (Gutierrez, Osman, Kopper, Barrios, & Bagge, 2000; Reynolds, 1991b). It also evidences predictive validity, with total score predicting suicide attempts over a 3-month follow-up period (Osman et al., 1999). In the current sample, coefficient alpha for the ASIQ was .97.
Analyses
A hierarchal logistic regression was used to examine the influence of a previous suicide attempt and severity of suicidal ideation on binge drinking group (social only = 0, solitary = 1). For these analyses, if a participant reported (a) no episodes of solitary binge drinking during a typical month in the past year, and (b) had at least one episode of social binge drinking, then they were classified as a social binge drinker (n = 129). If a participant reported a binge drinking episode while alone or when no one else was drinking at least once during a typical month in the past year, then they were classified as a solitary binge drinker (n = 53). Solitary binge drinkers also could have episodes of social binge drinking, and previous research has found that nearly all do engage in social binge drinking (Gonzalez, Collins et al., 2009).
Four separate hierarchical multiple regression analyses for frequency of drinking and drinks per drinking day in social and solitary contexts were conducted to examine the association of a previous suicide attempt and suicidal ideation with each drinking variable. The same independent variables were entered in the multiple regression analyses and logistic regression analysis described above. In the first step of the analyses, age, gender, and ethnicity were entered to control for their possible effects on drinking and suicidality variables. In the second step, suicide attempt history (no attempts = 0, previous attempt = 1) was entered. In the final step, severity of suicidal ideation was entered to examine whether suicidal ideation was significantly associated with the given drinking dependent variable after accounting for suicide attempt history.
Given that the data for the current research came from two studies, analyses were repeated, including data source (Study 1 vs. 2) in the models in Step 1 and mean-centered interaction terms of Data Source × Suicide Attempt History and Data Source × Suicidal Ideation included in a final step. The interaction terms allowed an examination of whether the degree of relationship between suicide attempt history and severity of suicidal ideation and the dependent variables differed between the samples in the two studies.
In order to improve normality and reduce the influence of outliers, suicidal ideation, frequency of social drinking, and drinks per social drinking day were square-root transformed. Frequency of solitary drinking and drinks per solitary drinking day, which were more substantially skewed, were log transformed.
ResultsIn the study sample, 22.6% (n = 12) of solitary binge drinkers and 8.5% (n = 11) of social binge drinkers reported a previous suicide attempt. Means, standard deviations, and correlations among the study variables are presented in Table 1. The majority of solitary binge drinkers also engaged in social binge drinking at least one day per month (94.3%). An analysis of covariance, controlling for age, gender, and ethnicity revealed that solitary binge drinkers engage in social binge drinking more days per month (M = 5.03, SD = 3.98) than individuals who only binge drink socially (M = 3.38, SD = 2.81; F(1, 177) = 10.01, p = .002, η2 = .05). Solitary binge drinkers reported a mean of 3.04 (SD = 3.97) solitary binge days per month. Among social binge drinkers, 50.4% engaged in solitary drinking at least one day a month.
Means, Standard Deviations, and Intercorrelations of Study Variables
The sequential logistic regression analysis revealed that having a previous suicide attempt was significantly associated with a greater likelihood of being a solitary binge drinker as opposed to only a social binge drinker (OR = 3.76, p = .006; see Table 2). Severity of suicidal ideation also was significantly associated with being a solitary binge drinker. In the third step of the analysis, suicide attempt history remained significantly associated with a greater likelihood of being a solitary binge drinker even when suicidal ideation was controlled for (OR = 2.76, p = .046), suggesting that both suicidality (suicidal ideation and attempts) variables were independently associated with a greater likelihood of being a solitary binge drinker.
Logistic Regression Predicting Being a Solitary Binge Drinker
Separate hierarchical multiple regression analyses were conducted to examine the association of suicidality with frequency of social drinking and with drinks per social drinking day. Frequency of drinking in social contexts was not significantly associated with a previous suicide attempt (ΔR2 < .001, p = .85) or with severity of suicidal ideation (ΔR2 = .005, p = .33). Similarly, drinks per social drinking day was not significantly associated with a previous suicide attempt (ΔR2 = .001, p = .61) or with severity of suicidal ideation (ΔR2 = .003, p = .47).
Given the higher suicidality among solitary binge drinkers, it was possible that social and solitary binge drinkers differed in the strength of association between social drinking context and suicidality. Therefore, a hierarchical regression analysis was conducted to examine potential differences between binge drinking groups in these associations. In the first step, age, gender, ethnicity, binge drinking group, previous suicide attempt, and suicidal ideation (mean centered) were entered into the model. In the second step, two cross-product interaction terms were entered: Binge Drinking Group × Suicide Attempt and Binge Drinking Group × Suicidal Ideation. The addition of these interaction terms did not add significantly to the regression models for frequency of social drinking (ΔR2 = .014, p = .28) or drinks per social drinking day (ΔR2 = .012, p = .30), indicating that there was not a significant difference between social and solitary binge drinkers. These analyses suggest that for both binge drinking groups, neither frequency nor amount of drinking in social contexts was significantly associated with suicidality.
The hierarchical multiple regression analyses examining the association of a previous suicide attempt and severity of suicidal ideation with solitary drinking frequency and drinks per solitary drinking day revealed that a previous suicide attempt was associated with more frequent solitary drinking (ΔR2 = .041, p = .004; see Table 3) and with more drinks per solitary drinking day (ΔR2 = .037, p = .008). Suicidal ideation was not significantly associated with frequency of solitary drinking once suicide attempt history was accounted for (ΔR2 = .012, p = .12). However, suicidal ideation was associated with more drinks per solitary drinking day (ΔR2 = .033, p = .010). Finally, repeating these analyses while controlling for data source (Study 1 vs. 2) revealed the same patterns of significance for the suicidality variables as reported above. The final step in the models containing the interaction terms of data source by suicide attempt history and suicidal ideation were nonsignificant in all analyses (ΔR2 between .002 and .015), suggesting that results found do not differ significantly by data source despite differences in the selection criteria between studies.
Multiple Regression Analyses Predicting Solitary Drinking
DiscussionThe findings of this study suggest that binge drinking students with a suicide attempt history are significantly more likely to engage in solitary binge drinking. Students with a previous suicide attempt were nearly four times more likely to be solitary as opposed to only social binge drinkers. Those experiencing greater severity of recent suicidal ideation also were more likely to be solitary binge drinkers. Both a previous suicide attempt and recent suicidal ideation were independently associated with a greater likelihood of solitary binge drinking.
A history of a suicide attempt was associated with more frequent solitary drinking and having more drinks per solitary drinking day. In contrast, suicide attempt history and severity of suicidal ideation were not associated with frequency of social drinking or the amount of alcohol consumed on social drinking days for either social or solitary binge drinkers. The findings are consistent with that of a previous study that found that severity of suicidal ideation was significantly associated with solitary, but not social, binge drinking among underage students with a history of suicidal ideation (Gonzalez, Collins et al., 2009), and extends the findings to emerging adult students who were not selected for their suicidal ideation history.
Future studies are needed to examine how or why suicidal ideation and a history of a suicide attempt are associated with solitary binge drinking. One potential way that a previous suicide attempt and suicidal ideation may be related to solitary binge drinking is through drinking to cope with negative affect. According to motivational models of alcohol use, drinking to cope is motivated by efforts to escape, avoid, or lessen negative affect (Cooper, Frone, Russell, & Mudar, 1995). Drinking to cope appears to motivate binge drinking in the absence of the social influences commonly associated with drinking among emerging adult students (Christiansen et al., 2002). Consistent with this notion, frequency of solitary binge drinking in a previous study was found to be associated with severity of suicidal ideation and motivated by drinking to cope (Gonzalez, Collins et al., 2009).
Motivational models of alcohol use also suggest that coping-skill deficits contribute to a reliance on alcohol to cope with negative affect (Cooper et al., 1995; Cooper, Agocha, & Sheldon, 2000; Cox & Klinger, 1988). Similarly, cognitive–behavioral models of suicidality note the important role of coping-skill deficits in suicidal ideation and behavior (Reinecke, 2006; Rudd, 2006). Consistent with these models, suicidal ideation and behavior and drinking to cope are associated with greater use of avoidant coping strategies (Britton, 2004; Edwards & Holden, 2001; Reinecke, DuBois, & Schultz, 2001; Williams & Kleinfelter, 1989). Individuals with a history of a suicide attempt may be more likely to engage in solitary binge drinking in order to cope with distress, as well as be more likely to suffer from distress compared with social binge drinkers, in part owing to poorer coping skills. Future studies are needed to examine this possibility, as well as other potential links between solitary drinking and suicidality.
An important limitation of this study was the cross-sectional design. Because of this it is not known whether suicidal ideation motivates solitary binge drinking, solitary binge drinking plays a causal role in suicide attempts, or if these relationships are reciprocal or indirect. Another limitation was the overrepresentation of women in the sample. This study also was limited to emerging adult college binge drinkers and therefore may not generalize to noncollege student emerging adults or nonbinge drinking students. Studies are needed to examine solitary binge drinking in relation to negative affect and suicidality with noncollege population samples.
In conclusion, the current findings suggest that solitary binge drinkers are in particular need of suicide prevention efforts. Binge drinking alone among individuals with a suicide attempt history and/or greater severity of suicidal ideation is alarming in regard to risk for suicidal behavior, as intoxication can impede adaptive coping, increase aggression, and worsen mood (Hufford, 2001). Solitary binge drinkers are in need of treatment efforts aimed at reducing their alcohol misuse, given their greater frequency of social binge drinking as well as additional episodes of solitary binge drinking. This may serve to reduce the likelihood for alcohol dependence, as well as suicide risk within this population.
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Submitted: April 7, 2011 Revised: December 7, 2011 Accepted: December 8, 2011
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Source: Psychology of Addictive Behaviors. Vol. 26. (3), Sep, 2012 pp. 609-614)
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Record: 9- 'Association of solitary binge drinking and suicidal behavior among emerging adult college students': Correction to Gonzalez (2012). Gonzalez, Vivian M.; Psychology of Addictive Behaviors, Vol 26(3), Sep, 2012 pp. 620. Publisher: American Psychological Association; [Erratum/Correction] Abstract: Reports an error in 'Association of solitary binge drinking and suicidal behavior among emerging adult college students' by Vivian M. Gonzalez (Psychology of Addictive Behaviors, Advanced Online Publication, Jan 30, 2012, np). In the article, there is an error in the introductory paragraph. The number of students who had seriously considered attempting suicide in the Barrios, Everett, Simon, & Brener (2000) study should have been reported as 11.4%, not 1.4%. Additionally, in the Participants section, data for the study were collected from March 2009 to September 2010, not March 2009 to January 2010 as reported. (The following abstract of the original article appeared in record 2012-02608-001.) Emerging adult college students who binge drink in solitary contexts (i.e., while alone) experience greater depression and suicidal ideation than do students who only binge drink in social contexts, suggesting that they may be at greater risk for suicidal behavior. This study examined the association of a previous suicide attempt, one of the best predictors of future suicide attempts and suicide, and severity of recent suicidal ideation with drinking in solitary and social contexts. Participants were binge drinking, emerging adult (18- to 25-year-old) college students (N = 182) drawn from two studies of college drinkers. A logistic regression analysis revealed that both suicide attempt history and severity of suicidal ideation were significantly associated with a greater likelihood of being a solitary binge drinker as opposed to only a social binge drinker. Students with a previous suicide attempt were nearly four times more likely to be solitary binge drinkers. Multiple regression analyses revealed that suicide attempt history was significantly associated with greater frequency and quantity of drinking in solitary, but not social contexts. Suicidal ideation was significantly associated with drinks per solitary drinking day, but not frequency of solitary drinking once suicide attempt history was accounted for. Given the associations found between solitary binge drinking and a history of suicide attempts, as well as greater severity of recent suicidal ideation, it appears that these students are in need of suicide prevention efforts, including treatment efforts aimed at reducing binge drinking. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Correction to Gonzalez (2012)
In the article “Association of Solitary Binge Drinking and Suicidal Behavior Among Emerging Adult College Students,” by Vivian M. Gonzalez (Psychology of Addictive Behaviors, Advance online publication, January 30, 2012. doi: 10.1037/a0026916), there is an error in the introductory paragraph. The number of students who had seriously considered attempting suicide in the Barrios, Everett, Simon, & Brener (2000) study should have been reported as 11.4%, not 1.4%. Additionally, in the Participants section, data for the study were collected from March 2009 to September 2010, not March 2009 to January 2010 as reported.
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Record: 10- Associations of descriptive and reflective injunctive norms with risky college drinking. Collins, Susan E.; Spelman, Philip J.; Psychology of Addictive Behaviors, Vol 27(4), Dec, 2013 pp. 1175-1181. Publisher: American Psychological Association; [Journal Article] Abstract: The current study describes the relative predictive power of descriptive norms (i.e., how much the target student believes referents 'drink until they get drunk') and reflective injunctive norms (i.e., target student’s perception of referents’ approval of the target student drinking until drunk) across various reference groups. The aim of this study was to gain further insight into which types of norms and reference groups are most highly concurrently correlated with risky drinking. It was hypothesized that both reflective injunctive and descriptive norms would be significantly positively correlated with risky drinking outcomes, and that more proximal reference group norms would be more highly predictive than more distal reference group norms. Participants (N = 837) were college students on the U.S. west coast who completed questionnaires in the context of a longitudinal parent study. Cross-sectional, zero-inflated negative binomial regressions were used to test the relative strengths of correlations between descriptive and reflective injunctive norms (i.e., for typical college students, closest friend, person whose opinion they value most, and closest family member) and risky drinking (i.e., peak alcohol quantity, frequency of heavy drinking episodes, and alcohol-related problems). Findings showed that descriptive and reflective injunctive norms were most consistently, strongly and positively correlated with risky drinking when they involved referents who were closer to the target college drinkers (i.e., closest friend and person whose opinion you value the most). Norms for typical college students were less consistent correlates of risky drinking. These findings may contribute to the knowledge base for enhancing normative reeducation and personalized normative feedback interventions to include more personally salient and powerful normative information. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Associations of Descriptive and Reflective Injunctive Norms With Risky College Drinking / BRIEF REPORT
By: Susan E. Collins
Department of Psychiatry and Behavioral Sciences, University of Washington – Harborview Medical Center;
Philip J. Spelman
Department of Psychiatry and Behavioral Sciences, University of Washington – Harborview Medical Center
Acknowledgement: Supported by a Small Grant from the Alcohol and Drug Abuse Institute (Grant No. 65-1951), a National Institute on Alcohol Abuse and Alcoholism (NIAAA) Career Transition Award (Grant No. K22 AA018384), and the NIAAA (Grant No. R01 AA012547).
The negative consequences associated with the consistently high rates of heavy episodic drinking (i.e., ≥ 4 drinks for women and ≥ 5 drinks for men; Dawson, Grant, Stinson, & Chou, 2004; Nelson, Xuan, Lee, Weitzman, & Wechsler, 2009) among college students are well-documented. Heavy episodic drinking affects both college drinkers (e.g., accidents and falls resulting in injury; risky sex; unwanted sexual advances, rape, and sexual assault; driving while intoxicated; problems with authorities; Abbey, 2002; Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002) and their communities (e.g., sleep disruption; property damage; verbal, physical, or sexual violence; Langley, Kypri, & Stephenson, 2003; Wechsler & Nelson, 2008). The pressing severity of the problem has led researchers to explore correlates and predictors of heavy-episodic drinking among college students—including family history, demographics, personality factors, drinking motives, expectancies, attitudes, peer influences, drinking onset, and, perhaps most prolifically, perceived norms—to better understand how to effectively intervene (Baer, 2002; Borsari, Murphy, & Barnett, 2007).
Dozens of studies have shown strong associations between norms and college drinking (see Borsari & Carey, 2003; Borsari & Carey, 2001, for reviews). Most of these studies have evaluated the importance of descriptive norms, or the target student’s beliefs about how much other reference groups drink, and have collectively shown that college students consistently overestimate referents’ drinking (Borsari & Carey, 2003). Descriptive norms have been shown to be positively associated with alcohol outcomes (e.g., Baer, Stacy, & Larimer, 1991; Larimer et al., 2009; Larimer et al., 2011; Larimer, Turner, Mallett, & Geisner, 2004; Perkins, 2007; Perkins, Haines, & Rice, 2005).
Injunctive norms, or how much the target student believes referents approve or disapprove of drinking in general, have been researched to a lesser extent, but studies have shown similar outcomes. Specifically, college students perceive reference groups to approve of drinking more than is actually the case (Borsari & Carey, 2003; DeMartini, Carey, Lao, & Luciano, 2011; LaBrie, Napper, & Ghaidarov, 2012). On the other hand, the association between injunctive norms and drinking outcomes is more complex. Studies have shown that self-other discrepancies (i.e., the perceived difference between the target’s and the referents’ behaviors and attitudes) created by injunctive norms are larger than for descriptive norms (Borsari & Carey, 2003). The association of injunctive norms with drinking outcomes varies by reference group: the closer the referents, the greater the positive association between perceived referent approval of drinking and the target student’s drinking and alcohol-related problems (LaBrie, Hummer, Neighbors, & Larimer, 2010; Neighbors et al., 2008).
Reflective injunctive norms are a more personally referenced version of injunctive norms, and they were originally introduced as “subjective norms” in the theory of reasoned action (Ajzen & Fishbein, 1980) and, later, in the theory of planned behavior (Ajzen, 1991).
Reflective injunctive norms represent the extent to which the target believes referents approve of the target’s own drinking (Ajzen, 2002). As applied in the theory of planned behavior, reflective injunctive norms have been shown to evince a positive association with intent to engage in risky drinking, and, in some studies, an indirect effect on risky drinking via intent (Collins & Carey, 2007; Collins, Witkiewitz, & Larimer, 2011; Huchting, Lac, & LaBrie, 2008; Johnston & White, 2003).
Current Study Aim and HypothesesThe aim of this study was to assess the relative predictive power of parallel descriptive and reflective injunctive norms across various peer groups and thereby to gain further insight into which types of norms and reference groups are most highly concurrently correlated with risky drinking. It was hypothesized that both reflective injunctive and descriptive norms would be significantly positively correlated with risky drinking outcomes (i.e., peak drinking quantity, heavy drinking episode [HDE] frequency, and alcohol-related problems), and that more proximal reference group norms would be more highly predictive than more distal reference group norms.
Method Participants
Participants were 837 (63.9% female, 0.1% transgender) college students at two, 4-year universities on the U.S. west coast who participated in a longitudinal parent study (response rate = 70%; Collins et al., 2011). Please see Table 1 for sample description.
Sociodemographic Sample Description (N = 837)
Measures
The Personal Information Questionnaire was used to assess participants’ age, gender, year in college, race and ethnicity, and membership in an on-campus Greek organization. This measure was used to describe the baseline sample.
The Reflective Injunctive Norms Questionnaire was made up of indicators from the Subjective Norms Questionnaire, a measure based on suggestions by Ajzen (2002), and modified from a previous study (Collins & Carey, 2007). Participants rated how much various referents would approve or disapprove of their “drinking until you get drunk” in the next 30 days, on a scale ranging from 1 (highly disapprove) to 5 (highly approve). Target groups included “an average college student at your university,” “your closest friend,” “your closest family member,” and “the person whose opinion you value most.” The question stem was rephrased to assess descriptive norms for each of the reference groups (e.g., “Will an average college student at your university drink until they get drunk at least once in the next 30 days?”). Participants could agree or disagree with these statements (1 = strongly disagree to 5 = strongly agree). Items for descriptive (α = .96) and reflective injunctive (α = .96) norms showed good internal consistency.
The Frequency−Quantity Questionnaire (adapted from Borsari & Carey, 2000; Collins, Carey, & Sliwinski, 2002; Dimeff, Baer, Kivlahan, & Marlatt, 1999) includes open-ended items assessing participants’ self-aggregated alcohol frequency and quantity of alcohol consumption in the past 30 days.
The Timeline Followback Questionnaire (TLFB; Sobell & Sobell, 1992) was used to aggregate HDE frequency in the past 30 days. The TLFB is a set of monthly calendars that allows for a retrospective evaluation of drinking for each day of the previous month.
Alcohol-related problems were assessed using the Rutgers Alcohol Problems Index (White & Labouvie, 1989), which asks participants how often (0 = never to 4 = more than 10 times) they experienced 23 drinking-related consequences over the previous 30 days. The overall score showed acceptable internal consistency (α = .94).
Procedure
Potential participants for the current study were emailed invitations. Invitations included the study URL and a randomly generated personal identification number with which participants logged into the secure study website. Participants provided informed consent and completed the baseline assessment, which included the measures noted above and took approximately 45 min. They were paid $20 for their time.
Data Analysis Plan
Three regression models were conducted in Stata, Version 11.2 to determine the relative contributions of reflective injunctive and descriptive norms in the prediction of risky drinking outcomes (i.e., alcohol quantity during the heaviest drinking occasion in the past 30 days [peak alcohol quantity], HDE, and alcohol-related problems). Because drinking outcome variables were overdispersed, positively skewed count/integer responses and evinced a preponderance of zeros, zero-inflated negative binomial (ZINB) models were used (Cameron & Trivedi, 1998). ZINB is a subset of generalized linear models for count outcomes that are positively skewed and have more zero responses than would be expected given the distribution. ZINB models two processes: the zero-inflated portion of the model, which is a Bernoulli trial to determine the probability that an observation is consistently zero, and a negative binomial portion of the model, which determines the association if the observation is a feasible count response predicted by the negative binomial distribution (Hardin & Hilbe, 2007).
ResultsDescriptive statistics for the predictors and outcomes are presented in Table 2, and zero-order correlations are listed in Table 3.
Descriptive Statistics for Predictors and Outcomes
Zero-Order Correlations (Bivariate Spearman’s ρ) Between Norms and Risky Drinking
ZINB Model Outcomes
Peak alcohol quantity
The omnibus model was significant, χ2(8, n = 831) = 87.51, p < .001, Vuong z = 7.68, p < .001, Nagelkerke pseudo R2 = .30. The negative binomial portion of the model indicated that descriptive norms for a typical college student inversely predicted peak alcohol quantity, whereas descriptive norms for a respondent’s closest friend positively predicted peak alcohol quantity. Reflective injunctive norms for one’s closest friend were positively associated with peak alcohol quantity (see Table 4 for model parameters). The zero-inflated portion of the model indicated that both descriptive and reflective injunctive norms for one’s closest friend inversely predicted zero inflation. Therefore, the greater the agreement that one’s closest friend would drink until drunk and approve of the respondent drinking until drunk, the lower the likelihood the student belonged to the “consistent zero,” or abstinent part of the distribution. Additionally, the greater the perceived approval by the person whose opinion they valued the most, the less likely respondents were to belong to the consistent zero group (see Table 4).
Zero-Inflated Negative Binomial Model Parameters
Zero-Inflated Negative Binomial Model Parameters
Heavy drinking episodes
The omnibus model was significant, χ2(8, n = 831) = 53.57, p < .001, Vuong z = 4.42, p < .001, Nagelkerke pseudo R2 = .30. The negative binomial portion of the model indicated that descriptive norms for a typical college student inversely predicted HDE, whereas descriptive norms for a respondent’s closest friend positively predicted HDE. Regarding reflective injunctive norms, similar relationships existed for norms regarding typical students and one’s closest friend (see Table 4). The zero-inflated portion of the model indicated that both descriptive and reflective injunctive norms for one’s closest friend inversely predicted zero inflation (see Table 4).
Alcohol-related problems
The omnibus model was significant, χ2(8, n = 797) = 16.45, p = .04, Vuong z = 4.01, p < .001, Nagelkerke pseudo R2 = .20. None of the negative binomial results was significant for predicting alcohol-related problems (see Table 4). The zero-inflated portion of the model indicated that descriptive norms for one’s closest friend and the person whose opinion the respondent valued most inversely predicted zero inflation (see Table 4).
DiscussionThe aims of this study were to test the relative predictive power of parallel descriptive and reflective injunctive norms across various peer groups. It was hypothesized that both reflective injunctive and descriptive norms would be associated with risky drinking outcomes (i.e., peak drinking quantity, HDE frequency, and alcohol-related problems), and that more proximal referents would be more highly predictive than more distal referents.
Summary of Current Findings
Findings largely corresponded to hypotheses. Zero-order correlations showed that both descriptive and reflective injunctive norms regarding closer referents (in descending level of association: closest friend, person whose opinion you value most, and closest family member) showed stronger positive correlations with risky drinking and related problems than norms regarding typical college students.
Next, our multivariate analyses afforded the unique opportunity to differentiate between two aspects of risky drinking: the odds of typically engaging in nonrisky versus risky drinking as well as the extent of risky drinking. Regarding the former, we were able to note which types of norms predicted drinking outcomes for people who were likely never to drink, experience HDE, or alcohol-related problems. Specifically, the norm for one’s closest friend was the primary predictor among descriptive norms, such that greater belief one’s closest friend would drink until they got drunk was associated with lower likelihood of being either abstinent or a lighter drinker. Greater conviction that both one’s closest friend and person whose opinion one values most would drink until drunk was also associated with a lower likelihood of nonproblem drinking. Regarding reflective injunctive norms, we found that greater agreement that one’s closest friend or person whose opinion one values most would approve of one drinking until drunk was associated with lower likelihood of being either abstinent or a light drinker.
Second, we examined the prediction of the extent of risky drinking by descriptive and injunctive norms. We found that descriptive and reflective injunctive norms for one’s closest friend served as the most consistent, positive predictors of risky drinking outcomes, with reflective injunctive norms for the person whose opinion respondents value most being a similarly consistent positive predictor. Unlike in the zero-order correlations, however, closest family member was never significantly associated with risky drinking outcomes, and neither descriptive nor reflective injunctive norm was associated with the extent of one’s experience of alcohol-related problems. Finally, descriptive norms regarding a typical college student were inversely—not positively—associated with risky drinking outcomes in the multivariate models.
Findings in the Context of the Norms Literature
Our findings replicated and extended those of other recent studies of norms and their relative weight across various reference groups (Larimer et al., 2009; Larimer et al., 2011) and norm types (LaBrie et al., 2010; Neighbors et al., 2007; Neighbors et al., 2008). Regarding the similarities between other studies and the current study, we noted that both descriptive and reflective injunctive norms using typical college students as the reference group were positively associated with risky drinking in zero-order correlations—although not as strongly positively correlated as closer reference groups. When examined in the context of other reflective injunctive and descriptive norms, however, norms for typical students were inversely associated with experience of HDE and peak alcohol quantity. A similar pattern of findings has been found across a few studies whose authors have attributed it to a potential negative suppressor effect (cf. Neighbors et al., 2007; Neighbors et al., 2008). Although more studies are needed to further parse and confirm this finding, it may be concluded that norms regarding closer referents and not typical college students are more powerful and consistent predictors of risky drinking outcomes.
Although it replicated findings from previous studies, the current study also expanded on these findings to include new reference groups (e.g., “person whose opinion you value most,” “closest family member”), a target behavior that is more specific to HDE experienced by college students (i.e., “drinking until you got drunk”), and a different type of injunctive norm (i.e., reflective injunctive norms) also known as “subjective norms” in the theory of planned behavior (Ajzen, 1991). Although reflective norms have been explored in a couple of prior studies in the college drinking norms literature, this has only been done in the context of opposite-sex perceptions and has not been personally referenced (Hummer, LaBrie, Lac, Sessoms, & Cail, 2012; LaBrie, Cail, Hummer, Lac, & Neighbors, 2009). Because they capture people’s perceptions of others’ approval of their own behavior, the current findings suggest reflective injunctive norms may be personally relevant and affectively salient.
Study Limitations
Because we did not additionally assess injunctive norms, it is impossible to compare the relative contributions of the reference groups’ approval of drinking until drunk in general (injunctive norms) versus approval of the target student’s drinking until drunk (reflective injunctive norms). Future studies may include both types of injunctive norms to understand their relative predictive abilities.
The norms questionnaires did not ask participants to indicate exactly who certain referents were (e.g., whom participants had in mind when considering their “closest family member” or “person whose opinion they valued most”). Nonetheless, we were able to address the research question: what types of norms are most predictive of risky drinking outcomes. Future studies may incorporate qualitative methods to describe these most salient referents in greater detail.
Finally, this study is a cross-sectional representation of the associations between college drinking and norms. To better understand how norms may affect college drinking trajectories, it will be important to replicate such findings in longitudinal studies. Despite these limitations, however, the current study was able to introduce new reference groups, a new target behavior, and a different type of injunctive norm. In doing so, we were able to replicate and expand on the existing college norms literature.
Conclusion and Future Directions
This study indicates that descriptive and reflective injunctive norms are most highly, positively correlated with college drinking when they involve referents who are closer to the target college drinkers (i.e., closest friend and person whose opinion you value the most). These findings may contribute to the knowledge base for enhancing normative reeducation and personalized normative feedback interventions to include more personally salient and powerful normative information.
Future studies should include assessment of both injunctive and reflective injunctive norms to better characterize the difference between these two aspects of social approval, and to understand whether one may be more affectively salient than the other. In turn, future studies of personalized normative feedback may include feedback on closer referents to create more powerful interventions that inspire greater and potentially more lasting drinking behavior change for college students. This suggested direction is challenging, because it is much more difficult to gather information on specific referents to assess the normative discrepancy. On the other hand, future studies could explore feedback on reflective injunctive norms without relying on normative discrepancy. It is possible that simply making college drinkers aware of their normative beliefs could build discrepancy (e.g., self-other or ideal current drinking) that could, in turn, influence drinking behavior.
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Submitted: September 14, 2012 Revised: March 11, 2013 Accepted: March 20, 2013
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Record: 11- Baseline reaction time predicts 12-month smoking cessation outcome in formerly depressed smokers. Kassel, Jon D.; Yates, Marisa; Brown, Richard A.; Psychology of Addictive Behaviors, Vol 21(3), Sep, 2007 pp. 415-419. Publisher: American Psychological Association; [Journal Article] Abstract: Burgeoning evidence points to a positive association between cigarette smoking and depression. Moreover, depressive symptomatology, whether historical, current, or subsyndromal, appears to negatively influence smoking cessation efforts. Whereas depression is typically assessed via clinical interview or self-report, rarely are the known neurocognitive deficits linked to depression (e.g., global slowing) assessed in the context of smoking cessation research. Hence, this study examined whether simple reaction time--color naming of affectively neutral words--is predictive of 12-month smoking cessation outcome among a sample of formerly depressed smokers (N = 28). Results revealed a significant, positive correlation between reaction time and depressive symptoms such that those who exhibited slower reaction times were at heightened risk to relapse. Baseline depressive symptoms, as assessed via self-report, neither correlated with nor predicted smoking cessation outcome. Results from logistic regression analyses further showed that reaction time added incremental variance to the prediction of smoking cessation outcome. Therefore, simple reaction time may capture aspects of depression not typically assessed in self-report questionnaires. These results are discussed in terms of their theoretical and clinical implications for smoking cessation research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Baseline Reaction Time Predicts 12-Month Smoking Cessation Outcome in Formerly Depressed Smokers
By: Jon D. Kassel
Department of Psychology, University of Illinois at Chicago;
Marisa Yates
Department of Psychology, University of Illinois at Chicago
Richard A. Brown
Butler Hospital, Providence, Rhode Island;
Department of Psychiatry and Human Behavior, Brown Medical School
Acknowledgement: This study was partially supported by National Institute on Drug Abuse Grant DA08511 to Richard A. Brown. We gratefully acknowledge Michelle Ricci for her able assistance in software programming. Portions of this article were presented at the fifth annual meeting of the Society for Research on Nicotine and Tobacco, San Diego, California, March, 1999.
The pathways to becoming a smoker are, no doubt, complex (e.g., Jamner et al., 2003; Kassel, Weinstein, Skitch, Veilleux, & Mermelstein, 2005). However, of the numerous factors believed to heighten vulnerability to smoking initiation, development of nicotine dependence, and smoking relapse, the role played by various forms of psychopathology and emotional distress appears particularly critical (Kassel, Stroud, & Paronis, 2003). That is, numerous studies have reliably found high smoking rates among selected populations of individuals with mental illness. For example, drawing upon a large, nationally representative sample in the United States, Lasser et al. (2000) found that individuals with a lifetime history of any psychiatric disorder had higher rates of lifetime and current smoking compared to individuals who had never suffered from mental illness. Indeed, other investigations have reported similar findings, demonstrating strong and reliable associations between psychiatric disorders—particularly depression (Kassel & Hankin, 2006)—and cigarette smoking among adults (Breslau, Kilbey, & Andreski, 1991; Covey, Glassman, & Stetner, 1998; Degenhardt & Hall, 2001).
Unfortunately, relapse is the modal outcome among those attempting to quit smoking (Piasecki, 2006). Whereas the best available treatments yield 1-year abstinence rates approaching 30–35%, even among smokers who successfully quit for a full year, as many as 40% eventually return to regular smoking (U.S. Department of Health and Human Services, 1990). Smokers who attempt to quit on their own fare even less well, with relapse rates ranging from 90% to 97% (Cohen et al., 1989; Hughes et al., 1992). Negative affect and symptoms of depression appear to be strongly implicated in these high relapse rates.
Identification of the extent to which depression predisposes to smoking relapse has emerged as a critical area of research inquiry in recent years. Indeed, a burgeoning research literature points to both strong between- and within-person associations between depressive symptoms and smoking relapse (see Kassel & Hankin, 2006; Kassel et al., 2003). The presence of clinically significant levels of negative affect and depressive symptoms is frequently predictive of relapse (Glassman et al., 1990; Hall, Munoz, Reus, & Sees, 1993). For instance, one study reported that the likelihood of quitting smoking was 40% lower among depressed smokers compared with nondepressed smokers (Anda et al., 1990). Glassman et al. (1990) found a quit rate of 14% for those meeting criteria for major depression, whereas 31% of participants with no psychiatric diagnosis successfully quit. Moreover, even in the absence of current symptomatology, history of depression may serve to heighten risk for both relapse (Covey, 1999; Glassman et al., 1993; though see Hitsman, Borrelli, McChargue, Spring, & Niaura, 2003) and recurrence of depressive symptomatology subsequent to cessation (Covey, Glassman, & Stetner, 1997). Indeed, history of recurrent major depression (i.e., having two or more past episodes) appears to be a particularly robust predictor of poor smoking cessation outcome (Brown et al., 2001; (Haas, Munoz, Humfleet, Reus, & Hall, 2004). Niaura et al. (2001) demonstrated that even low (subclinical) levels of depressive symptoms assessed at baseline among smokers enrolled in a cessation program were predictive of time to first cigarette smoked after attempted quitting.
One potential limitation of the rather extensive database examining the link between depression and smoking relapse is that depression (be it at the clinical or subclinical level) has been primarily assessed via clinical interview or self-report. There are clearly problems inherent in sole reliance on these types of measurement strategies. Although self-report measures are simple, inexpensive, and easy to use, they are susceptible to biases, social expectations, and attributions, and can ultimately measure only one's conscious experience of depressive symptomology. Correspondingly, a large literature demonstrates that depression—even depressive symptoms that fall short of clinical diagnosis—is reliably associated with a host of neurocognitive deficits (e.g., Porter, 2003; White, Myerson, & Hale, 1997). For example, Pardo, Pardo, Humes, and Posner (2006) recently conducted a study assessing depressed and nondepressed individuals' performance on tasks measuring phasic alerting and covert orientation of visuospatial attention. Their findings suggest that global slowing is a major cognitive deficit in depression. In fact, such findings support the results of a meta-analysis demonstrating that task-independent, generalized global slowing accounts for the apparent specific cognitive deficits found in unipolar depression (White et al., 1997).
Global slowing may be best viewed as a manifest difference in speeded trials between two comparison groups (Pardo et al., 2006). That is, global slowing represents a very real deficit in processing speed among individuals, revealed by comparison with an appropriate reference group. However, one should not conceive of global slowing as a phenomenon necessarily related to specific disorders (e.g., depression) or groups of people (e.g., older adults). Rather, global slowing has been observed across diverse samples, including sleep-deprived individuals, those who abuse central nervous system depressants (e.g., barbiturates and alcohol), and individuals with other psychiatric disorders, such as schizophrenia (Ryan, Russo, & Greeley, 1996).
Whereas psychomotor and cognitive slowing of the kind reported above are widely recognized as critical components of the depression syndrome, rarely are such cognitive measures utilized in the context of smoking cessation research. Because there is also reason to believe that, in recent years, the remaining population of smokers is hardening (i.e., becoming more nicotine dependent or exhibiting more comorbid psychopathology, including depression; Warner & Burns, 2003), it is incumbent upon tobacco researchers and clinicians alike to further elucidate the admittedly complex association between depressive symptomatology and smoking cessation. Toward this end, we prospectively examined the relationship between reaction time—an index of processing speed—and smoking cessation outcome in a group of formerly depressed smokers. Specifically, we were interested in ascertaining whether a simple measure of reaction time would correlate with a self-report measure of depression and add to the predictive utility of a self-report measure of depression in predicting long-term smoking cessation outcome.
Method Participants
Participants in the present study were recruited from the community and were already participating in a large treatment outcome study (N = 179) comparing standard smoking cessation treatment with treatment incorporating cognitive–behavioral therapy for depression (see Brown et al., 2001 for details). For the present study, 28 randomly selected participants agreed to take part in an investigation examining “cognitive factors related to smoking cessation success.” These smokers (mean age = 48.8 years, 58% female) had smoked an average of 29 years (SD = 10.4), smoked a mean of 28 cigarettes a day (SD = 14.3), had a mean Fagerstrom Test for Nicotine Dependence (FTND; Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991) score of 7.4 (SD = 1.6), reported an average of 7.7 previous smoking cessation attempts (SD = 11.1), and presented with a mean Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) score of 8.7 (SD = 5.7). As assessed with the Structured Clinical Interview for DSM-III-R-Non-Patient Edition (SCID-NP; Spitzer, Williams, Gibbon, & First, 1990), all participants met diagnostic criteria for past, but not present, history of major depression.
Procedures
Participants were administered an emotional Stroop task (see Williams, Mathews, & MacLeod, 1996) prior to quitting smoking (T1) and immediately after their target quit date (T2). The emotional Stroop task assesses verbal response latency to colored words presented on a computer screen. Words either were of neutral semantic content (e.g., pencil, table, and carpet) or were emotionally laden target words (e.g., depression-related words, such as sad, dejected, and worried; anxiety-related words, such as nervous, worried, and anxious; anger-related words, such as angry, mad, and hostile; and smoking-related words, such as cigarette, smoke, and ashtray). For the purposes of the present analyses, we considered only subjects' response times (RTs; time to name out loud the color of presented words) to neutral, nonemotionally laden words at T1. As such, the assessed RTs served as a rather pure index of processing speed, unencumbered by potential emotional interference effects or by nicotine withdrawal (evident at T2).
After brief instruction and a practice trial conducted during the T1 visit, participants were presented a total of 120 (20 neutral) interspersed words, displayed one at a time on a video graphics array monitor in red, green, yellow, or blue. Their task was to indicate the color in which respective words appeared by saying the color of the word (thereby ignoring the semantic content of the stimulus) out loud as quickly as they could. Response latency was recorded via a microphone linked to a Macintosh software program designed to detect and record verbal latency to the nearest millisecond.
Thus, our primary dependent variable was 7-day point-prevalence abstinence rate (Hughes et al., 2003) assessed at 12 months posttreatment. The intent-to-treat principle was used, as was biochemical verification (expired air carbon monoxide and salivary cotinine), in order to confirm 12-month smoking status. Hence, participants were categorized as either abstinent or nonabstinent at 12 months posttreatment. Predictor variables included RT to name the color of only the affectively neutral words, nicotine dependence (FTND), and baseline (T1) depressive symptoms (BDI).
ResultsEven though we are interested in only the reaction time to the emotionally neutral words for the purposes of the present article, to place the RT measure in broader context, Figure 1 displays reaction times to all word categories at both T1 and T2. Initial correlational analyses are in Table 1. As anticipated, RT was significantly and positively correlated with depressive symptomatology at Visit 1 (p < .01); slower response times were associated with higher BDI scores. Moreover, RT was significantly correlated with 12-month point-prevalence abstinence rates (p < .01), such that slower response times to the naming of the color of neutral words were associated with a greater likelihood of nonabstinent smoking status. To explore the possibility that the observed relationship between RT and smoking outcome was simply attributable to the significant correlation between depressive symptoms and RT, we conducted a partial correlational analysis, in which we assessed the relationship between RT and 12-month outcome, while controlling for T1 BDI scores. The correlation remained significantly negative (pr = −.41, p < .04).
Figure 1. Reaction time displayed by visit (Visit 1 = prequit, Visit 2 = postquit) and word category. Error bars represent standard error of the mean. Dep = depression; Anx = anxiety; Smok = smoking.
Correlation Matrix of Variables
Depressive symptoms at both T1 and the 12-month follow-up were modestly, though nonsignificantly, associated with smoking outcome in the anticipated direction (both ps < .14); higher depressive symptoms were associated with smoking outcomes of nonabstinence. Of note, nicotine dependence (FTND) was not significantly correlated with 12-month smoking status (p = .11).
Outcome data revealed that of the 28 participants, 18 were nonabstinent at 12 months posttreatment, yielding a success rate of 36%. Next, we conducted an independent sample t test, in order to see whether 12-month smoking status was influenced by RTs assessed at Visit 1. Consistent with the correlational analysis, results revealed that those who were smoking (nonabstinent) at 12 months evidenced significantly higher RTs (M = 818.83 ms, SD = 120.91) relative to those who were abstinent (M = 692.54 ms, SD = 92.96), t(26) = 2.86, p = .008 (seeFigure 2).
Figure 2. Reaction time displayed by 12-month smoking cessation outcome (abstainers vs. nonabstainers). Error bars represent standard error of the mean.
Finally, to derive a better sense of the predictive utility of the RT measure (and based on the conceptual and statistical associations between RT and depressive symptoms), we conducted a stepwise binary logistic regression analysis, in which baseline depression (BDI–T1) served as the predictor variable in the first block, followed by RT entered in the next block; 12-month smoking cessation outcome was the dependent variable. Whereas analyses of the model with BDI alone revealed reasonable goodness-of-fit, Hosmer and Lemeshow test, χ2(6, N = 28) = 4.51, p = .61, the omnibus test of model coefficients was only marginally significant, χ2(1, N = 28) = 2.90, p = .09 (Nagelkerke R2 = .14). Further, the odds ratio yielded by the BDI was 0.86, with an overall classification rate of 64% of participants correctly classified as either abstinent (4/10, 40%) or nonabstinent (14/18, 78%). Inclusion of RT in the model also yielded an adequate goodness-of-fit index, Hosmer and Lemeshow test, χ2(7, N = 28) = 10.90, p = .14. However, with the addition of RT in the model, the omnibus test reached significance, χ2(2, N = 28) = 8.51, p < .02 (Nagelkerke R2 = .36). The overall classification rate reached 75%, with 83% of nonabstinent (15/18) and 60% of abstinent (6/10) individuals correctly classified (odds ratios of .96 and .97 for the BDI and RT measures, respectively). Moreover, univariate tests showed that whereas RT was a significant predictor of smoking cessation outcome (Wald = 3.89, p < .05), the BDI was not (Wald = 0.12, p > .70).
DiscussionIt is well established that there are strong and reliable associations between depression and smoking (Kassel & Hankin, 2006). Moreover, this relationship appears particularly strong in the context of smoking relapse; those smokers who are currently depressed, or even symptom-free but present with a history of depression, may be at greater risk for relapse subsequent to cessation (Wilhelm, Wedgwood, Niven, & Kay-Lambkin, 2006). Acknowledging that these data are based on measures of depressive symptomatology almost entirely derived from clinical interview and self-report, we hoped to improve on this situation by incorporating a measure of simple reaction time in the current study. The rationale here was that neurocognitive slowing has emerged as a potent marker of depressive symptomatology in and of itself (e.g., Pardo et al., 2006; White et al., 1997) and hence could potentially add discriminant validity to the prediction of smoking cessation outcomes.
As anticipated, we found that RT was positively correlated with depressive symptomatology, as measured by the BDI. Thus, both measures appear to be tapping shared aspects of depression. More important, however, RT was found to independently predict 12-month smoking cessation outcomes. Furthermore, this finding was derived from a sample of smokers who were not currently depressed but rather shared a history of at least one major depressive episode. Also of note was the observation that, whereas RT did predict cessation outcome, the BDI did not. Partial correlational analysis revealed that even when statistically controlling for the potential influence of the BDI, the association between RT and smoking status 12 months later held up. Hence, RT appears to be tapping an aspect of depression critical to its influence on smoking cessation. As such, future research needs to explore the mechanisms underlying these links.
Several limitations of the present need to be acknowledged. First, the sample size was relatively small. Thus, the extent to which our findings can be replicated with larger samples needs to be determined. Second, the sample was restricted to smokers with a history of major depressive disorder, none of whom met criteria for current major depression. Therefore, it remains to be seen whether slowed RT serves as a vulnerability factor for relapse among smokers without a history of depression or for smokers who enter treatment while in the throes of a major depressive episode. Correspondingly, it is conceivable that RT may actually have served as a proxy for some broader or related constructs, distinct from depression, for example, distress tolerance (Brown, Lejuez, Kahler, Strong, & Zvolensky, 2005) or task persistence (Brandon et al., 2003). Hence, in the absence of a control group comprising smokers without a history of depression, it becomes difficult to definitively attribute the observed RT differences to neurovegetative aspects of depression. Third, we used only one measure to assess neurocognitive functioning. Whereas it has been argued that depressive symptoms are associated with task-independent cognitive slowing (Pardo et al., 2006), future research should nonetheless further explore the association observed in this study by utilizing different types of cognitive tasks to see if, indeed, more specific cognitive deficits are predictive of cessation outcomes.
Finally, it is important to note that whereas the finding that RT predicts smoking status 12 months later is clearly important and, as far as we are aware, the first of its kind, more work needs to be done with respect to better understanding the underlying mechanisms that govern depression–smoking associations (see Kassel and Hankin, 2006, and Kassel et al., 2003, for a discussion of these issues). The present study demonstrated that a distal predictor influenced outcome many months later, but precisely how this relationship operates at the level of the individual smoker is as yet unknown. Indeed, delineation of within-subject, dynamic processes that contribute to smoking relapse (and relapse to other drugs and addictive behaviors as well) awaits future scrutiny (Shiffman, 2005).
In sum, this study suggests that one particular manifestation of depression—neurocognitive retardation as measured by reaction time on a simple decision task—accurately predicted treatment outcome 1 year later for 75% of our sample. Though these results need to be replicated and extended in future studies, one implication of our findings is that an RT measure could serve as a quick and cost-efficient method by which to assess aspects of depressive symptomatology not tapped by self-report measures. Indeed, the importance of fully understanding the link between smoking relapse and depression cannot be overstated. In recent years, virtually all state-of-the-art smoking cessation programs have come to incorporate some aspect of cognitive–behavioral therapy specifically aimed at reducing depressive symptoms (Wilhelm et al., 2006). Moreover, pharmacotherapy in the form of antidepressant medication has emerged as a first-line, effective approach to treating the smoker who desires to quit (Fiore et al., 2000). Because today's population of smokers is hardening, according to some researchers, and presenting with comorbid psychopathology, such as depression, it is critical that tobacco researchers continue to clarify the relationship between smoking and depression, as future lives are clearly at stake.
Footnotes 1 Thirty consecutive study participants were asked to partake in this supplemental study, of whom 2 declined.
2 Whereas the overall treatment outcome study from which this sample was derived comprised two treatment conditions (see Brown et al., 2001), no main effects for treatment were found in the full sample. Nonetheless, we assessed whether treatment condition influenced any of the findings reported in the present study; in all instances, inclusion of treatment condition did not change the reported findings.
3 During the RT task, errors to the neutral words were exceedingly rare, occurring, on average, less than once per participant. As such, there were no response speed–accuracy trade-off effects for RTs to the neutral words (r = −.05, p > .80).
4 The overall classification rate of 75% should be understood in context such that, even in the absence of any independent variables, the correct classification rate approaches 64%. Hence, RT ultimately allows for the correct prediction of 3 more individuals than would be seen using no predictors at all.
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Submitted: May 31, 2006 Revised: November 7, 2006 Accepted: November 8, 2006
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Source: Psychology of Addictive Behaviors. Vol. 21. (3), Sep, 2007 pp. 415-419)
Accession Number: 2007-13102-016
Digital Object Identifier: 10.1037/0893-164X.21.3.415
Record: 12- Between- and within-person associations between negative life events and alcohol outcomes in adolescents with ADHD. King, Kevin M.; Pedersen, Sarah L.; Louie, Kristine T.; Pelham, William E. Jr.; Molina, Brooke S. G.; Psychology of Addictive Behaviors, Vol 31(6), Sep, 2017 pp. 699-711. Publisher: American Psychological Association; [Journal Article] Abstract: Escalations in alcohol use during adolescence may be linked with exposure to negative life events, but most of this research has focused on between-person associations. Moreover, adolescents with attention-deficit hyperactivity disorder (ADHD) may be an especially vulnerable population, reporting more life events and alcohol involvement and may even be more sensitive to the effects of life events on alcohol outcomes compared with those without ADHD. We tested the between- and within-person effects of the number and perceptions of negative life events on the development of alcohol use outcomes from age 14 to 17 years in 259 adolescents with and without ADHD using generalized estimating equations. Between-person differences in exposure to negative life events across adolescence, but not the perception of those events, were associated with a higher likelihood of alcohol use and drunkenness at age 17 years. Within-person differences in life events were associated with alcohol use above and beyond that predicted by an adolescents’ typical trajectory over time. Parent- and teacher-reported ADHD symptoms were associated with more negative perceptions of life events and with greater alcohol use and drunkenness at age 17 years, but symptoms did not moderate the life event–alcohol association. Interventions should consider the variables that produce vulnerability to life events as well as the immediate impact of life events. That the accumulation of life events, rather than their perceived negativity, was associated with alcohol outcomes indicates that interventions targeting the reduction of negative events, rather than emotional response, may be more protective against alcohol use in adolescence. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Between- and Within-Person Associations Between Negative Life Events and Alcohol Outcomes in Adolescents With ADHD
By: Kevin M. King
Department of Psychology, University of Washington;
Sarah L. Pedersen
Department of Psychiatry, University of Pittsburgh
Kristine T. Louie
Department of Psychology, University of Washington
William E. Pelham Jr.
Departments of Psychology and Psychiatry, Florida International University
Brooke S. G. Molina
Departments of Psychiatry, Psychology and Pediatrics, University of Pittsburgh
Acknowledgement: This research was principally supported by grants from the National Institute on Alcohol Abuse and Alcoholism: AA011873, AA007453, and AA00202. Additional support was provided by DA12414, MH50467, MH12010, ESO5015, AA12342, DA016631, MH065899, KAI-118-S1, DA85553, MH077676, MH069614, MH62946, MH53554, MH069434, IES LO3000665A, IESR324B060045, and NS39087. Sarah L. Pedersen was supported on grants from the National Institute on Alcohol Abuse and Alcoholism (K01AA021135) and ABMRF/The Foundation for Alcohol Research.
These hypotheses and analyses have not been presented in any form prior to this article.
Alcohol use disorder is most prevalent between ages 18 and 29 years (Grant et al., 2015), but the development (e.g., the initiation of use and the first appearance of problems) of these disorders begins earlier in adolescence (Meich, Johnston, O’Malley, Bachman, & Schulenberg, 2015). As the average quantity and frequency of alcohol use increases in the general population from adolescence into young adulthood, variability in alcohol use also steadily increases with age, with some adolescents escalating rapidly into heavy drinking and eventually problems (e.g., Hussong, Bauer, & Chassin, 2008), whereas others remain light or moderate drinkers. Only a fraction of those who begin drinking in adolescence eventually develop an alcohol use disorder, with prevalence estimates of the past year of any alcohol use disorder between ages 18 and 29 years at approximately 26% (Grant et al., 2015). As such, understanding what influences the developmental trajectories of alcohol involvement across adolescent development remains a priority of research.
Although a large literature has outlined the pathways by which externalizing behaviors may influence the development of adolescent alcohol involvement (see review by Chassin, Colder, Hussong, & Sher, 2016), emerging evidence suggests that exposure to negative life events may independently shape alcohol use trajectories and presage worsening outcomes (Keyes, Hatzenbuehler, Grant, & Hasin, 2012). Negative life events predict higher levels of alcohol use, alcohol-related problems, and alcohol use disorders among adolescents and young adults (Cerbone & Larison, 2000; King & Chassin, 2008; Wills, Sandy, & Yaeger, 2002). Prospective studies that have focused on change over time in alcohol use suggested that exposure to negative life events was associated with escalating trajectories of alcohol use during adolescence (King, Molina, & Chassin, 2009; Wills, Sandy, Yaeger, Cleary, & Shinar, 2001).
This prior research on the negative life event–alcohol use association focused on between-person associations, in which those who report more negative life events at an earlier time point exhibited, on average, higher levels or greater increases in alcohol use and problems over time. However, most hypotheses about the role of negative life events in alcohol use focus on within-person processes, hypothesizing that alcohol use occurs when an individual is both exposed to negative life events and utilizes maladaptive coping strategies in the face of those negative life events (Chassin et al., 2016; Sher, Grekin, & Williams, 2005). Some individuals are more likely to experience negative life events, either because of contextual or individual factors such as parenting, temperament, or socioeconomic status (King, Molina, & Chassin, 2008). Thus, it is important to disaggregate the between-individual effects of exposure, which may reflect more stable individual differences in the propensity to experience negative life events, from the within-individual effects of the life events themselves, which may better represent the process of stress adaptation as well as other time-varying factors that influence both stress and drinking (Curran & Bauer, 2011).
To date, one study showed that time-varying differences in negative life events were related to time specific increases (i.e., those not accounted for by an adolescent’s average trajectory of use) in alcohol use and binge drinking during adolescence (Aseltine & Gore, 2000), but that study did not explicitly separate the within- and between-person associations of life events with alcohol use (Enders & Tofighi, 2007). Using a latent growth curve modeling framework, a second study showed that family life events were related to between- and within-person differences in alcohol use during adolescence (King et al., 2009). However, more recent methodological studies have suggested that some of the methods of that study, such as binning ordinal measures of alcohol use frequency (McGinley & Curran, 2014) or failing to disaggregate between- from within-person variance as predictors in growth models (Curran, Howard, Bainter, Lane, & McGinley, 2014), may have inflated the time-varying associations in those models. Finally, neither prior study accounted for the heavily skewed and zero-inflated nature of adolescent alcohol use (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013).
Attention-Deficit Hyperactivity Disorder (ADHD), Negative Life Events, and Alcohol Use
Substantial evidence suggests that adolescents with ADHD are at a heightened risk of experiencing negative life events: ADHD is associated with academic and social difficulties that may directly increase the likelihood of experiencing negative life events (e.g., getting bad grades, fighting with peers [Barkley, 2006]). Moreover, many children with ADHD continue to meet diagnostic criteria in adolescence (e.g., Barkley, Murphy, & Fischer, 2008), and even more experience impairment despite subclinical levels of ADHD symptoms (Sibley et al., 2012). Continued ADHD-related impairments may also indirectly increase the experience of other negative life events (e.g., parental decisions to restrict activities or resources). Finally, children and adolescents with ADHD come from families in which they are exposed to higher levels of negative life events, such as exposure to marital conflict, divorce, general family adversity, and parental alcoholism (Counts, Nigg, Stawicki, Rappley, & von Eye, 2005; Knopik et al., 2006; Wymbs et al., 2008). It may be that adolescents with a diagnosis of ADHD report higher levels of negative life events because of the downstream effects of early ADHD (such as diminished peer relations), the background variables associated with a diagnosis of ADHD (such as family problems), or the effects of continued impairment from ADHD.
Adolescents with ADHD not only may report greater exposure to negative life events, but they may also be especially sensitive to their effects. Their diminished skills for coping with distress (Hampel, Manhal, Roos, & Desman, 2008; Molina, Marshal, Pelham, & Wirth, 2005), among other vulnerability characteristics such as weak executive function and generally increased impulsivity, support this hypothesis. One study from our group found a stronger cross-sectional association between academic life events (e.g., doing poorly on an exam) and problem alcohol use for adolescents with, versus without, ADHD histories (Marshal, Molina, Pelham, & Cheong, 2007). Relatedly, other widely studied environmental factors, such as peer alcohol use (Marshal, Molina, & Pelham, 2003) and parental monitoring (Walther et al., 2012), have been shown to be more strongly related to alcohol use for those with ADHD compared with those without ADHD. Understanding the potential for stress vulnerability in ADHD is particularly important, given the increased risk for alcohol use disorder that characterizes this population in adulthood (e.g., Lee, Humphreys, Flory, Liu, & Glass, 2011).
Whereas much prior research on negative life event exposure and alcohol use has utilized simple count measures of life events (which require an adolescent to report whether an event occurred), research on negative life events highlights the importance of considering individuals’ perception of events (Grant et al., 2003). For example, Davis and Compas (Davis & Compas, 1986) found that the desirability of a life event (whether rated as negative or positive) was positively related to students’ perception that they could cope with events (r = .84). Moreover, the mere number of stressors and how stressors are perceived may explain different variation in psychopathology (Duggal et al., 2000). Classic models of stress and psychopathology argue that stressors should be perceived as a challenge or threat to the individual (Lazarus & Folkman, 1984), although reviews of the stress literature show that both counts of stressors and an individuals’ perception of those stressors can be useful (Grant et al., 2004). To date, most research has relied on counts of stressors and has not considered whether an adolescent’s perception of stressors explains variance in alcohol use. Adolescents with ADHD are known to overestimate their competence and underestimate their impairment across a number of life domains (Evangelista, Owens, Golden, & Pelham, 2008; Hoza et al., 2004). It may be that adolescents with ADHD report a higher number of life events but (due to cognitive biases) report that they are less impactful or upsetting and that their perception of events differentially alters the relation between negative life events and alcohol outcomes.
Current Study
The current study extends prior research by examining the between- and within-person associations of negative life events and alcohol outcomes in a sample of adolescents with and without a well-established diagnosis of ADHD during childhood, using statistical methods that better account for the skewed and zero-inflated nature of those outcomes. The main goal of the current study was to replicate prior work while attempting to explicitly address the methodological challenges raised in recent studies as well as extending these models to a new high-risk sample, adolescents with ADHD. We hypothesized that both between- and within-individual differences in exposure to negative life events would be associated with alcohol use. The second goal of the current study was to test whether children diagnosed with ADHD reported greater numbers of negative life events during adolescence and to examine the contribution of concurrent ADHD symptoms to negative life events. Third, we aimed to test whether adolescents with ADHD showed stronger associations between life events and alcohol behaviors during adolescence. We hypothesized that ADHD history would predict the experience of negative life events and, most importantly, strengthen the association between life events and alcohol outcomes. Finally, we compared simple counts of negative life events with the adolescent’s perception of the negativity of those events.
Method Participants
More detailed information on the recruitment of the Pittsburgh ADHD Longitudinal Study (PALS) may be found in another report (Molina et al., 2012).
ADHD group
Participants with childhood ADHD were diagnosed with Diagnostic and Statistical Manual of Mental Disorders III, revised (DSM–III–R) or DSM–IV ADHD in childhood, at an average age of 9.40 years (SD = 2.27). Participants with ADHD were selected for longitudinal follow-up with annual interviews because of their diagnosis of ADHD and participation in a summer treatment program for children with ADHD, an 8-week intervention that included behavioral modification, parent training, and psychoactive medication trials where indicated (Pelham & Hoza, 1996).
Participants with ADHD were assessed in childhood using standardized parent and teacher DSM–III–R and DSM–IV disruptive behavior disorder symptom rating scales (Pelham, Gnagy, Greenslade, & Milich, 1992) and a standardized semistructured diagnostic interview administered to parents by a PhD-level clinician. Two PhD-level clinicians independently reviewed all ratings and interviews to confirm DSM diagnoses, and when disagreement occurred, a third clinician reviewed the file and the majority decision was used. Exclusion criteria for follow-up was assessed in childhood and included a full-scale intelligence quotient <80, a history of seizures or other neurological problems, and/or a history of pervasive developmental disorder, schizophrenia, or other psychotic or organic mental disorders. At the first PALS follow-up interview, which occurred on a rolling basis between 1999 and 2003, the mean age was 17.75 years (SD = 3.39 years, range = 11–25).
Non-ADHD group
Adolescents without ADHD were recruited into the PALS when those with ADHD were recruited for follow-up. Non-ADHD comparison participants were recruited on a rolling basis to ensure demographic similarity to the ADHD group (age within 1 year, sex, race, highest parental education) and were recruited from the same regional area as the participants with ADHD. Individuals who met DSM–III–R criteria for ADHD (presence of eight or more symptoms reported by either the parent or young adult participant), currently or historically, were excluded. Non-ADHD comparison participants with subthreshold ADHD symptomatology, or with other psychiatric disorders, were retained.
Procedure
Interviews for the PALS were conducted annually in adolescence. Interviews were conducted in the ADD Program offices by postbaccalaureate research staff. Informed consent was obtained and all participants were assured confidentiality of all disclosed material except in cases of impending danger or harm to self or others (reinforced with a Department of Health and Human Services Certificate of Confidentiality). In cases in which distance prevented participant travel to the research offices, information was collected through a combination of mailed and telephone correspondence; home visits were offered as need dictated. Self-report questionnaires were completed either with paper and pencil or Web-based versions on a closed circuit Internet page. All procedures were approved by the Institutional Review Board of Western Psychiatric Institute and Clinic.
Selection of the Current Sample
Data were selected from the first four annual interviews of adolescents based on procedures used elsewhere to test longitudinal hypotheses about adolescent functioning (Molina et al., 2012). Participants were selected if they were interviewed one or more times between the ages of 14 and 17 years. Because multilevel modeling and generalized estimating equations make use of all available data at Level 1 (Raudenbush & Bryk, 2002), participants were excluded only if they were not interviewed between ages 14 and 17 years. For the resulting subsample (n = 259), there were no statistically significant differences between the ADHD (n = 146) and non-ADHD (n = 113) groups on sex or ethnic/racial minority but a statistically significant difference for highest parental education and household income (lower in the ADHD group). For analysis, data were organized by age at interview to allow modeling of life events and alcohol use longitudinally by age (Bollen & Curran, 2006). This provided data for life events and alcohol use at one (n = 43), two (n = 79), three (n = 86), or four (n = 51) occasions. Participants provided data at ages 14 (n = 114), 15 (n = 158), 16 (n = 166), and 17 years (n = 167). To estimate between-person associations, we had data from 259 participants with 756 observations. To estimate within-person associations, we had data from 216 participants (with 689 repeated observations), 129 of whom (with 388 observations) reported any alcohol use. Table 1 provides descriptive statistics for the current sample.
Demographics of Current Sample (n = 259)
Measures
Background variables: Parental characteristics, sex, and race
Because they have been shown to influence the occurrence of life events, alcohol use, or both, for all analyses we initially controlled for the baseline presence of a parental alcohol use disorder, parental antisociality, maternal depressive symptoms, parental divorce, sex, and race. Parental alcohol use disorder was coded as present if either parent met criteria on the Structured Clinical Interview for DSM for Nonpatients (Spitzer, Williams, Gibbon, & First, 1990) by their own report or was reported by the other parent on the Michigan Alcoholism Screening Test–Short (Selzer, Vinokur, & van Rooijen, 1975). The parent on the Michigan Alcoholism Screening Test–Short focuses on consequences from problematic drinking, and a score of 3 or higher was coded as having an alcohol use disorder. These two assessments were combined and coded as 1, either parent met criteria based on self/other-report, vs. 0, neither parent met criteria based on self/other-report. Parental antisociality was coded as present if either parent met criteria on the Structured Clinical Interview for DSM for Nonpatients (Spitzer et al., 1990). Maternal depressive symptoms were assessed by maternal self-report on the 21-item Beck Depression Inventory (Beck, Steer, & Carbin, 1988). Sex was self-reported (0, female, 1, male), as was race (0, White, 1, non-White).
Negative life events
Negative life events in the past year were assessed annually with 120 items from the Adolescent Perceived Events Scale (Compas, Davis, Forsythe, & Wagner, 1987). The Adolescent Perceived Events Scale has been used extensively in prior research and has been shown to predict both internalizing and externalizing symptoms in adolescents (Grant et al., 2003; McMahon, Grant, Compas, Thurm, & Ey, 2003). For each item, adolescents indicated whether an event occurred and, using a 9-point scale, the degree to which it was experienced as negative or positive (1, extremely bad, to 9, extremely good). Example life events were parents getting divorced, parent loses a job, having few or no friends, not getting along with parents of friends, doing poorly on an exam or paper, problems or arguments with teachers or principal, getting in trouble or being suspended from school, death of a family member, change in the health of a friend, and hospitalization of a family member or relative. We excluded 31 items from the original scale that assessed minor life events (such as going to church/synagogue or helping other people), psychological symptoms, bereavement/illness (which also occurred very rarely), substance use, or ADHD diagnosis or treatment, leaving 89 total negative life events. Following prior work with this scale (Wagner & Compas, 1990), we computed both a count of negative life events and a score reflecting the subjective evaluation of those negative events. Prior research has indicated that both the accumulation of life events as well as the adolescent’s perception of them are independent predictors of psychopathology (Grant, Compas, Thurm, McMahon, & Gipson, 2004). The count was comprised of all items rated by the adolescent as at least slightly bad (4) to extremely bad (1). The subjective evaluation score was computed as the mean of the adolescent’s ratings of all events that were rated as at least slightly bad after reverse scoring the ratings (e.g., extremely bad [4]). Across age, these two scores were correlated very weakly, r = .18, p < .001).
Table 1 provides descriptive statistics for negative life events. On average, adolescents reported approximately 11 different negative life events in the past year (range = 0–58) and reported an average perception of 2.36, which represents a response of somewhat bad.
ADHD symptoms
Childhood diagnosis of ADHD is described above. At each wave, ADHD symptoms were measured using parent and teacher report of 18 DSM–IV ADHD symptoms (Pelham, Gnagy, Greenslade, & Milich, 1992), scored on a scale of 0 (not at all) to 3 (very much). We then took the maximum score across the two raters for each symptom and computed a mean across all symptoms at each age.
Alcohol Use
Alcohol use was assessed at each annual interview with a structured paper-and-pencil substance use questionnaire (Molina & Pelham, 2003; Molina, Pelham, Gnagy, Thompson, & Marshal, 2007). The substance use questionnaire is an adaptation of existing measures, including the Health Behavior Questionnaire (Jessor, Donovan, & Costa, 1989) and the National Household Survey on Drug Abuse interview (Substance Abuse and Mental Health Services Administration, 1992) and includes both lifetime exposure questions (e.g., have you ever had a drink, age of first drink) and quantity/frequency questions for alcohol and other substances. The current study utilized two items that assessed frequency of use and drunkenness over the past 12 months. Items used a 12-point scale (from never to several times a day). We tested alcohol outcomes separately because there are concerns in the literature about combining across alcohol outcomes and/or converting them to pseudocount variables (as we did previously; King et al., 2009), particularly when they are measured with an ordinal scale (McGinley & Curran, 2014).
Analytic Strategy
We were interested in predicting between-person differences in negative life events during adolescence from childhood and adolescent ADHD symptoms and in predicting within- and between-person differences in alcohol outcomes during adolescence from life events and ADHD. However, our alcohol outcomes were heavily skewed and zero-inflated, in that many adolescents did not report drinking, and when they did, most reported fairly low levels. This produces nonnormality in the residuals, violating the assumptions of multilevel models (MLMs) and can produce bad parameter estimates and misleading inferences (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013). Thus, to predict alcohol outcomes, we used generalized estimation equations (GEEs; Zeger, Liang, & Albert, 1988), GEE readily allows the estimation of zero-inflated and hurdle count models (such as zero-inflated Poisson or hurdle negative binomial models), which may better estimate the response generation process for these variables while also accounting for the effects of clustering within the individual. MLMs and GEEs were well suited as analytic approaches because both estimate parameters using the available Level 1 data (i.e., repeated observations of individuals across age), do not require all Level 2 observations (i.e., participants) to have identical or balanced observations at Level 1 and readily allow the separation of between- and within-components of variance in predictors and outcomes (Raudenbush & Bryk, 2002).
We tested our hypotheses in R 3.2.3 using the nlme and pscl packages with the maximum likelihood estimator. We used MLMs to predict negative life events across adolescence and GEEs to predict alcohol outcomes. To estimate GEEs, we estimated generalized linear models with the appropriate link function (such as negative binomial or hurdle negative binomial) using the r package pscl (Zeileis, Kleiber, & Jackman, 2008) and used a custom sandwich estimator to correct model standard errors for clustering (D. Hu, personal communication). This corrects the standard errors of the fixed effects for the effects of clustering while avoiding the problems that can arise from trying to model the random covariance structure (i.e., slope and intercept variability) of complex distributions such as zero-inflated count distributions. Because our hypotheses were related to fixed effects only (such as estimating the associations between ADHD and the slope of alcohol use), rather than on obtaining a population level estimate of individual differences in slopes, GEEs were an appropriate choice to test the current hypotheses.
Zero-inflated hurdle models
Although alcohol outcomes were measured on an ordinal scale, we utilized models for count data because they best fit the distributions of the alcohol variables. Across all alcohol outcomes, model fit indices (Bayesian information criterion and Akaike information criteria) suggested that zero-inflated or hurdle-negative binomial models best fit the data; we chose hurdle negative binomial models because they best fit our interpretation of adolescents’ actual behavior. Hurdle negative binomial models separately model the presence or absence of the outcome (i.e., the hurdle, or likelihood) and, among those with any level of the outcome, the count (or level) of the outcome as a negative binomial distribution, which has a variance that is greater than its mean (Hilbe, 2011). Thus, for each outcome, coefficients predicting the likelihood of the outcome occurring (i.e., whether or not an adolescent reported drinking in the past year) may be transformed into an odds ratio (OR), which predicts the relative odds of an event occurring. Coefficients predicting the level of an outcome are converted to a rate ratio (RR), which predicts the number of events (such as the number of drinks in the past year that an adolescent may report).
Centering of life events
To disaggregate the between- and within-person associations of negative life events with alcohol outcomes, we used a combination of centering within cluster at Level 1 and grand-mean centering at Level 2 (Enders & Tofighi, 2007), which perfectly separates variation in a given predictor into within- and between-person variability. Centering within cluster is achieved by subtracting a participant-level mean across observations from each participant’s score at each time point. This provides a time-specific score that reflects only within-person variance, and observations at each time point essentially become a deviation score, representing that person’s deviation from their own average at that time point. The participant’s mean score across observations may be grand-mean centered by subtracting each participant’s mean from the sample average of all participant means, which can then be entered as their between-person variable. This score represents a participant’s average deviation from the sample mean and reflects their average level of life event exposure across adolescence. These resulting centering within cluster and grand-mean centered scores are perfectly uncorrelated (r = .00) because they partial within- and between-person variance in life event exposure over time. In this way, a multilevel model may be utilized to address state-trait questions by a simple centering scheme.
Model-fitting approach
We followed a standardized approach to model fitting. For all model comparisons, we relied on Akaike information criteria and Bayesian information criterion as tests of relative model fit (Raftery, 1995) prior to applying the GEE correction to account for clustering within subjects. We first tested for the general shape of change for both alcohol outcomes, comparing linear and quadratic models. Because there was little variability in alcohol outcomes at age 14 years, we used age 17 years as the intercept and estimated growth from age 14 to 17 years, as in our previous research (Molina et al., 2012). To ensure that the main hypothesis tests were not biased by unmodeled dependencies in the data, we tested all covariate by predictor interactions as well as the between- by within-person effects of both count and perceptions of life events. This is recommended as best practice for model building in regression models (Allison, 1977), and simulations have shown that not including or estimating interactions that exist in models can induce substantial bias in the main effects coefficients (Vatcheva, Lee, McCormick, & Rahbar, 2015).
To balance the risk of alpha inflation against model misspecification, we used an a priori threshold of p < .01 to retain significant covariate by predictor interactions and refrained from interpreting any interactions we did retain to avoid speculation about nonhypothesized interactions. Then we examined the main effects of between- and within-person negative life events on each outcome. Next, we tested whether childhood ADHD was associated with life events during adolescence and compared those models with ones measuring the effects of concurrent ADHD symptoms. Finally, we tested whether childhood diagnosis and concurrent symptoms of ADHD moderated the between- and within-person associations of life events with each alcohol outcome. Again, because we had only general hypotheses about this effect (i.e., that life events would have a stronger relation for those with ADHD or more ADHD symptoms), we used the Benjamini-Hochberg correction (Benjamini & Hochberg, 1995) to control for the false discovery rate. We initially controlled for parental alcoholism, antisociality, and depression in all analyses, but controlling for them did not change the magnitude of the coefficients or inferences from the final models, so we dropped them for the sake of parsimony. Thus, the final models controlled for sex, race, and parental divorce to control for baseline differences.
Results Descriptive Statistics
Adolescents with and without childhood diagnoses of ADHD reported similar counts and perceptions of life events (all t(277) < 1.28, p > .20), averaging approximately 11 negative life events and reporting them to be somewhat bad on average. Among those with a childhood ADHD diagnosis, the average ADHD symptom score from age 14 to 17 years was 1.51 (SD = .63), whereas the average ADHD symptom score for those without a childhood diagnosis was lower (M = .48, SD = .37), t(277) = −15.42, p < .001.
Only two covariate-by-predictor interactions were significant at our a priori threshold of p < .01. A significant interaction of race (White vs. non-White) by divorce (nondivorced vs. divorced parents) (p = .0022) suggested that the effects of parental divorce on the likelihood of any alcohol use were smaller for non-White adolescents (OR = .05) than for White adolescents (OR = .60). Because these interactions were not hypothesized, we do not interpret them further, but we did include them in all further models to reduce model misfit and to improve coefficient estimation.
Unconditional Models of Alcohol Involvement
We illustrate the unconditional growth models in Figure 1 to aid interpretation. For all alcohol outcomes, a linear effect of time best fit the data, with all fit indices for the quadratic model greater than those for the linear models, suggesting the linear model of time fit better than the quadratic. Unconditional model results, with estimates of intercepts and slopes, are presented in Table 2. Generally, the likelihood of alcohol involvement across each outcome increased over time, with the odds of reporting any alcohol use or drunkenness increasing by 1.7–1.9 per year from age 14 to 17 years. In other words, for every year that passed, the likelihood of any alcohol use or drunkenness nearly doubled. For example, the probability of reporting any alcohol use rose from less than 20% at age 14 years to more than 50% by age 17 years, whereas the probability of reporting any drunkenness rose from less than 10% to around 35%.
Figure 1. Unconditional growth models of past year alcohol involvement. a, frequency of use; b, frequency of drunkenness. Model predicted probability and level of alcohol outcomes by age. Confidence intervals were simulated using the simcf package (C. Adolph, personal communication).
Unconditional Growth Model
There were also linear increases in the frequency of alcohol use over time, with drinking increasing by 18–19% per year (controlling for the influence of the covariates) among those who reported any alcohol use. On the other hand, those who reported any drunkenness exhibited no increases in the level over time, even as other adolescents became more likely to drink in that manner.
Negative Life Events and Alcohol Involvement
We then examined the between- and within-person associations of negative life events with alcohol use during adolescence, predicting the likelihood and level of alcohol outcomes from between- and within-person variability in the number and perception of life events.
The most consistent effect we observed was that between-person differences in the average count of negative life events over time were associated with higher age 17 year likelihoods of alcohol use (OR = 1.10) and drunkenness (OR = 1.069). Figure 2 illustrates the effects of life events on trajectories of the likelihood of alcohol use. Additionally, within-person variance in the number of negative life events was related to an increased likelihood of drunkenness (OR = 1.045) and a higher level (RR = 1.035) of alcohol use, meaning that in any given year, reporting more negative life events than expected was related to higher likelihoods of reporting drunkenness and more frequent alcohol use than what would be predicted by that adolescent’s own trajectory of use. Moreover, after correction for the false discovery rate, we observed an association of within-person variance in the number of negative life events and the likelihood of any alcohol use (OR = 1.033, p = .06).
Figure 2. Between-person differences in life event exposure predicts heightened probability of alcohol use over time. Model predicted probability of alcohol use by age at −1, mean and +1 SD of between-person count life event exposure. Confidence intervals were simulated using the simcf package (C. Adolph, personal communication).
On the other hand, the adolescent’s perception of those life events was not related to either drinking outcome at either the between- or within-person level.
Predicting Trait-Negative Life Events From ADHD
We next tested whether childhood ADHD predicted a larger number or worse perception of negative life events during adolescence using MLM. There was substantial between (54%) and within-person (46%) variability in adolescents’ report of both the number and perception of negative life events. None of the covariates were related to the perception or number of negative life events across adolescence. Moreover, only the effect of ADHD on the perception of negative life events approached significance (b = .152, SE = 0.082, p = .063), which suggested that adolescents with ADHD reported marginally more negative perceptions of negative life events from age 14 to 17 years but did not report any more or fewer negative life events relative to adolescents without ADHD.
This effect seemed to be confirmed in separate models using adolescent ADHD symptoms as a predictor of life events: The average level of ADHD symptoms across adolescence was associated with reporting more negative perceptions (but not higher numbers) of negative life events (b = .12, SE = .05, p = .035). In other words, higher levels of average ADHD symptoms across adolescence were related to more negative perceptions of life events on average.
Does ADHD Moderate the Effects of Life Events on Alcohol Involvement Across Adolescence?
Finally, we tested whether the relation between counts or perceptions of negative life events on alcohol use differed, depending on childhood ADHD diagnosis or adolescent ADHD symptoms. Table 3 presents these final results. We tested this hypothesis by including ADHD diagnosis or adolescent ADHD symptoms (in separate models) as predictors of level and change in alcohol involvement and as moderator(s) of the between- and within-person effects of life events described above. There were no main effects of childhood ADHD or moderation of life events by childhood ADHD that survived correction for the false discovery rate. This suggested that there was little support for the notion that childhood ADHD moderated the effects of life events on alcohol use.
Effects of Negative Life Events and ADHD on Alcohol Involvement
Between-person differences in ADHD symptoms during adolescence were associated with higher levels of alcohol use frequency (RR = 1.31) and drunkenness (RR = 1.53) at age 17 years. Among those who reported average or high levels of average ADHD symptoms across adolescence, the level of alcohol outcomes rose accordingly. There were no other main effects of ADHD symptoms during adolescence, nor did ADHD symptoms moderate the effects of life events on alcohol outcomes. Moreover, the associations between life events and alcohol use were largely unchanged with the inclusion of ADHD symptoms.
DiscussionThe goals of the current study were to extend prior research on the between- and within-person associations between negative life events and alcohol involvement during adolescence in a high-risk sample using a broad measure of life events and statistical models that better accounted for between- and within-person variability in life events as well as the nonnormal distributions of alcohol outcomes in adolescence. We tested whether ADHD was associated with heightened vulnerability to negative life event exposure and whether ADHD predicted a stronger association between negative life event exposure and alcohol involvement. Overall, our results largely suggested that the number, but not the perception, of negative life events was associated with both between- and within-person changes in the level or likelihood of alcohol involvement during adolescence. Only ADHD symptoms that persisted across adolescence were associated with more negative perceptions of life events (but not the number of life events) as well as with higher levels of alcohol use and drunkenness. Neither childhood diagnosis of ADHD nor persistence of ADHD symptoms strengthened the association between life event exposure and alcohol involvement.
Previous work (King et al., 2009) suggested that both between- and within-person exposure to uncontrollable stressors (familial life events) were related to trajectories of alcohol use. We partially replicated and extended this finding, showing that between-person differences in the number of negative life events were associated with an increased likelihood of alcohol involvement. Specifically, adolescents who reported an average number of negative life events that was 1 SD above the mean across adolescence also reported a likelihood of any drinking that was nearly twice as high (OR = 1.96; obtained by multiplying the model coefficient by the SD of between-person count of life events and then exponentiating) and a likelihood of getting drunk that was greater than 1.5 times as high (OR = 1.61) as an adolescent at the mean number of life events. Conversely, adolescents whose perceptions of life events were 1 SD more negative than average were no more likely to drink (OR = 1.04) or report getting drunk (OR = 1.05) than those whose perceptions were at the sample average. One interpretation of this finding is a third-variable explanation: Adolescents who are prone to experience negative life events, such as those with high levels of personality risk, or those in environmental contexts that expose them to high levels of adversity over time, are also more likely to drink and get drunk. Alternately, these findings may suggest that negative life events may be impactful because of their occurrence, rather than by their perception by the adolescent (Duggal et al., 2000). Altering appraisals of negative life events may be less effective than interventions that might seek to either reduce the number of negative life events themselves (by reducing controllable negative life events, such as by improving social skills) or by providing environmental supports that may counter the effects of uncontrollable negative life events whether or not an adolescent perceives them to be negative (e.g., increasing involvement in prosocial activities). Interestingly, most research and theory on interventions to address stress among youth emphasize improved individual’s coping or emotion regulation skills (Izard, 2002), but our findings lead us to speculate that supplemental approaches that counter the loss of resources that accompany accumulated negative life events (e.g., transportation to extramural activities needed after parental job loss; tutoring to raise poor grades) might be especially helpful. A growing literature on interventions that directly addresses ADHD-related impairments in adolescence is also relevant (Sibley et al., 2016). Future studies contrasting these approaches, and their associations with alcohol and other health risk behaviors, are warranted.
We also observed a relatively consistent within-person association: above and beyond the variance explained by age, when adolescents reported more negative life events than what was typical for them, in that same year they had higher likelihoods of drunkenness and higher levels of alcohol use that were not explained by their developmental trajectories of alcohol involvement (the association with the likelihood of alcohol use approached significance). Adolescents who reported life events in a year that were 1 SD above the average number of life events they reported across the study also reported 15% higher levels (RR = 1.15) of alcohol use, and 1.20 times the odds (OR = 1.20) of reporting drunkenness in that year, relative to the expected level and likelihood at their average number of life events. On the other hand, the relative associations of within-person fluctuations in perceptions with the level of alcohol use and the likelihood of drunkenness were much smaller and not significant (RR = .91, OR = .89, respectively). This finding extends our previous work (King et al., 2009) by showing that the associations of a broader range of negative life events beyond the relatively narrow range of family related life events captured by that earlier study are related to increased risk. Future interventions/preventions may directly benefit from these results. For example, alcohol prevention/intervention efforts could focus on adolescents who report a recent life event (e.g., such as parental divorce or school transitions) because this may be a time when alcohol use will subsequently increase. Moreover, our findings suggest that it is the events themselves, not the adolescents’ perception of them, that explain the within-person associations of negative life events with alcohol outcomes. Future research should explore the degree to which these state associations generalize to other forms of externalizing and internalizing psychopathology and whether other time varying factors (such as social support or coping skills) may moderate the associations of life events with psychopathology to guide the target of intervention. However, we should also caution that these within-person associations, which represent retrospective associations at the yearly level, cannot determine the true direction of effect; studies with a more time-sensitive design can bring us closer to an understanding of the connections between stress and alcohol use in the moment.
In general, adolescents with a childhood diagnosis of ADHD were no different in terms of their experience of the number or perception of negative life events between ages 14 and 17 years. However, between-person differences in current ADHD symptoms (i.e., parent- and teacher-reported symptoms during adolescence) were associated with a more negative perception of negative life events. Although a proliferation of studies have shown that adolescents and young adults with ADHD histories perceive less symptomatology and impairment than reported by their peers or parents (e.g., Mrug, Hoza, & Bukowski, 2004), this positive self-perception bias may not fully extend to perception of negative life events as stressful. This finding does not rule out the possibility that adolescents with ADHD actually underreport the occurrence of negative life events but perceive those that do occur as more negative. This differential finding may suggest that it is inattention to event occurrence that explains positive biases. Both symptoms of and impairment from ADHD for many adolescents continue to persist into adolescence (Barkley et al., 2008; Sibley et al., 2012), and these impairments may contribute to the perception of negative life events as more negative or stressful, particularly in the familial, school, and social domains as adolescents navigate the challenges of developing autonomy and individuation from parents, increasingly challenging school and social demands (Bagwell, Molina, Pelham, & Hoza, 2001; Kent et al., 2011). A number of studies have shown that symptom persistence in adolescence is associated with other externalizing problems such as oppositional defiant disorder and conduct disorder (i.e., Costello & Maughan, 2015), emotion problems, suicidality, and academic failure and dropout (Costello & Maughan, 2015; Kessler et al., 2014) as well as early adult substance use (Howard et al., 2015) including, in this sample, an association between ADHD symptom persistence, delinquency, and frequency of alcohol use (Molina et al., 2012, and replicated in the current study).
On the other hand, neither these life event perceptions, nor the experience of the negative life events themselves, were more strongly associated with alcohol outcomes for adolescents with a history of ADHD or with ongoing ADHD symptoms. These results conflict with prior work suggesting that adolescents with ADHD may be more susceptible to environmental conditions in regard to alcohol use (e.g., peer alcohol use, parenting factors, Belendiuk, Pedersen, King, Pelham, & Molina, 2016; Marshal et al., 2007; Walther et al., 2012). The differences between prior studies and the current one may reflect the longitudinal nature of the current study, the emphasis on the between- and within-individual differences in negative life events, or the treatment of alcohol use as a zero-inflated count outcome. It may also be important to consider additional dimensions of life event perception beyond positivity and negativity, such as the impact a life event has on an adolescent, or how important an adolescent views the event itself. Further research utilizing multiple informants of life events (e.g., school records, parental report) would also be useful as a check on the role of positive self-perception bias on our findings. In addition, a direct comparison of the effects of parent-reported impairments from ADHD, with the most typical being academic, behavioral, and social (Barkley et al., 2008), with those of self-reported negative life events associated with the experience of these impairments would further specify sources of alcohol use vulnerability for adolescents with ADHD. Mechanistic studies of negative affect, versus impairment-driven, pathways could follow (Molina & Pelham, 2014).
It should also be considered that both our null and significant findings could have been influenced by low power to detect effects in the current study, particularly at the within-person level, and that lack of support for certain effects (especially interactions) may at best suggest that effects that do exist may be smaller than the significant effects we were able to detect. Those effects were only estimable using data from individuals with more than one time point (n = 216 participants with 689 repeated observations, and only 129 of those participants reported any alcohol use). It is not well understood what study or design factors influence statistical power in GEEs, especially for hurdle count GEEs, and there are no guidelines for standard measures of effect size for count models in terms of what constitutes a small, medium, or large effect size. The relatively low variability in alcohol outcomes in the current study may have influenced our ability to detect associations, and thus, the null effects we report may not be reliably ruled out unless they are replicated in other samples. Moreover, some studies have raised the concern that effect size estimates from smaller samples, even if statistically significant, may be unreliable (Kraemer, Mintz, Noda, Tinklenberg, & Yesavage, 2006). Although it is not clear whether 689 repeated observations should be considered small for a GEE with a hurdle-negative binomial outcome, given the low variability in alcohol outcomes, this possibility should be considered, and it would be important to replicate the current findings to determine the degree to which the effect size estimates are reliable.
There are several strengths to the current study. First, we modeled our alcohol outcomes in a way that accounted for the heavily zero-inflated and skewed nature of the data and avoided combining across outcomes when doing so has been shown to produce misestimation (McGinley & Curran, 2014). Our application of multiple methods (such as testing between- and within-individual effects of negative life events, count and perceived life events, and childhood vs. concurrent ADHD) allowed a more nuanced examination of the current hypotheses. This is particularly important in light of the increasing awareness of p-hacking (Simmons, Nelson, & Simonsohn, 2011), practices that bias research studies toward presenting positive findings. As such, we intentionally presented all of our findings across all operationalizations of predictors and outcomes and used a relatively conservative approach to alpha correction with the Benjamini-Hochberg correction to provide a clear and hopefully reliable picture of how and when ADHD and negative life events are associated with alcohol use.
At the same time, several limitations warrant acknowledgment. Moreover, whereas we utilized zero-inflated hurdle models to account for the nonnormal distributions in alcohol outcomes, the alcohol items were ordinal in nature and not true counts. It may be that ignoring this may have inflated the estimates of alcohol use in the current study. Second, our reliance on self-report, particularly of life events, may have resulted in an underreporting in these events, particularly by the ADHD group. Moreover, we collapsed multiple categories of negative life events (both major and minor and life events of different sources). Although the goal here was to measure a general sense of the negative life event load, it could be that more precise findings could be obtained by a more fine-grained analysis of the effects of subcategories of negative life events. Unfortunately, there are few theoretically driven approaches to categorizing life events (but see Pillow, Barrera, & Chassin, 1998), and doing so for the current manuscript would have dramatically increased the risk of alpha inflation.
Despite these limitations, the current study adds significantly to the literature by examining negative life events with multiple different approaches over time. Specifically, these findings highlight the complexity of the negative life events–alcohol association and indicate the importance of examining both the number of life events as well as the perception of how negative these events are to the adolescent. Furthermore, future efforts focused on decreasing alcohol use the year following a negative life event may help reduce the escalation of adolescent alcohol use. Lastly, understanding how negative life events relate to alcohol use for adolescents with ADHD underscores the possibility that targeting negative life events, acute as well as chronic, may ultimately decrease risk for alcohol use disorder in this at risk population.
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Submitted: January 28, 2017 Revised: May 11, 2017 Accepted: May 12, 2017
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Record: 13- Childhood cognitive measures as predictors of alcohol use and problems by mid-adulthood in a non-Western cohort. Luczak, Susan E.; Yarnell, Lisa M.; Prescott, Carol A.; Raine, Adrian; Venables, Peter H.; Mednick, Sarnoff A.; Psychology of Addictive Behaviors, Vol 29(2), Jun, 2015 pp. 365-370. Publisher: American Psychological Association; [Journal Article] Abstract: This study examined the relationship between childhood cognitive functioning and academic achievement and subsequent alcohol use and problems in a non-Western setting. We examined longitudinal data from a birth cohort sample (N = 1,795) who were assessed at age 11 years on cognitive measures and then approximately 25 years later on lifetime alcohol use and alcohol use disorder symptom count. The sample was from Mauritius (eastern Africa), which allowed us to examine these relationships in a non-Western society with a different social structure than is typical of prior cognitive studies on primarily White samples in Western societies. Poorer performance on the Trail Making Test B-A in childhood predicted being a lifetime drinker, even after covarying for gender, childhood psychosocial adversity, and Muslim religion. Lower academic achievement and verbal IQ, but not performance IQ, were predictive of subsequent alcohol problems after including demographic covariates; the relationship between verbal IQ and alcohol problems was stronger in females than males. A nonlinear relationship emerged for Trails, suggesting that only more extreme impairment on this measure was indicative of later alcohol problems. Results of this study provide evidence that verbal deficits and poor academic performance exist in a general cohort sample by age 11 years (when 99% were nondrinkers) for those who go on to develop alcohol problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Childhood Cognitive Measures as Predictors of Alcohol Use and Problems by Mid-Adulthood in a Non-Western Cohort / BRIEF REPORT
By: Susan E. Luczak
Department of Psychology, University of Southern California, and Department of Psychiatry, University of California, San Diego;
Lisa M. Yarnell
Department of Psychology, University of Southern California
Carol A. Prescott
Department of Psychology and Davis School of Gerontology, University of Southern California
Adrian Raine
Departments of Criminology, Psychology, and Psychiatry, University of Pennsylvania
Peter H. Venables
Department of Psychology, University of York
Sarnoff A. Mednick
Department of Psychology, University of Southern California
Acknowledgement: Supported by the National Institutes of Health (Grants K08 AA14265, R01 AA10207, and R01 AA18179), the Mauritian Ministry of Health, the Medical Research Council, and the Wellcome Trust. We thank the staff of the Joint Child Health Project for their assistance with data collection and management, the Joint Child Health Project participants for their lifelong participation in this project, and John L. Horn, PhD, for his mentorship on this study.
Developmental models of the etiology of alcohol problems have proposed several pathways for how childhood cognitive deficits relate to later alcohol problems. Deviance proneness models purport that underlying behavioral dysregulation manifests in part as cognitive deficits and poor academic performance in childhood and later as alcohol problems in adulthood (see Gorenstein & Newman, 1980; Sher, 1991; Zucker, Chermack, & Curran, 2000). Alternative models have proposed more direct paths—that high intelligence and academic success can lead to both increased likelihood of being a lifetime drinker by placing individuals in heavier drinking environments (e.g., college) as well as decreased likelihood of developing alcohol problems via better opportunities in adulthood that may buffer against problems (see Johnson, Hicks, McGue, & Iacono, 2009). Conversely, pressure to maintain academic achievement may result in distress (e.g., Stoeber & Rambow, 2007), which in turn can lead to alcohol use and problems (Crum et al., 2006; Schulenberg, Bachman, O’Malley, & Johnston, 1994). It is also possible that multiple processes are involved, with both behavioral undercontrol and contextual factors contributing to the link between early cognitive deficits and later alcohol involvement.
General population sample studies have demonstrated that relatively higher IQs are typically found among low-to-moderate drinkers compared with abstainers and heavy drinkers (see Anstey, Windsor, Rodgers, Jorm, & Christensen, 2005; Müller et al., 2013, for reviews). In longitudinal studies, higher childhood IQ has been positively associated with alcohol use and higher consumption levels in early and later adulthood (Johnson, Hicks, McGue, & Iacono, 2009; Kanazawa & Hellberg, 2010). For example, in a U.S. national sample of young adults assessed over a 5-year interval, higher verbal IQ predicted increased risk for subsequent drinking and decreased risk for problems among drinkers even after covarying for socioeconomic status (Windle & Blane, 1989). This is consistent with a prospective study of a general Scottish sample that found higher verbal IQ at 11 years was associated with alcohol problems 40 years later after covarying for socioeconomic position (Batty, Deary, & Macintyre, 2006). Taken together, these studies suggest that higher IQ, and in particular verbal IQ, is predictive of increased likelihood of being a lifetime drinker and decreased likelihood of alcohol-related problems later in life, and that these associations are not accounted for by sociodemographic correlates of higher verbal abilities.
Academic achievement is also a potential childhood cognitive predictor of subsequent alcohol involvement. Support for poor academic achievement associated with subsequent alcohol use and problems also has been found in longitudinal studies and general samples with varying ranges of follow-up (see, e.g., Duncan, Duncan, Biglan, & Ary, 1998; Hawkins, Catalano, & Miller, 1992; Schulenberg et al., 1994). For example, Hayatbakhsh, Najman, Bor, Clavarino, and Alati (2011) demonstrated that poorer school performance at age 14 years predicted alcohol problems 21 years later in a general sample of Australian students, although already at age 14 over 60% of the sample indicated drinking in the past week. Studies that examine academic achievement in younger samples are needed to help clarify if poor academic achievement is a predictor of subsequent alcohol involvement or just a consequence of early alcohol use.
In this study, we present longitudinal data from a birth cohort sample that was assessed at age 11 years on measures of cognitive and academic ability (prior to the typical age of onset of alcohol use) and then approximately 25 years later for lifetime alcohol use and alcohol use disorder (AUD) symptoms. The sample was from the island of Mauritius (a middle-income eastern African nation), allowing for the examination of these relationships in a non-Western society that values education and academic performance (the population has an 89% literacy rate and public primary and secondary education are free; Central Intelligence Agency, 2013; Southern and Eastern Africa Consortium for Monitoring Educational Quality [SACMEQ], 2012), but where childhood cognitive performance is not linked to heavy drinking environments as it often is in Western societies (e.g., college; Slutske et al., 2004). However, as in Western societies, both intelligence and school success in Mauritius may enable individuals to obtain financial and personal resources that increase the likelihood and opportunities for social drinking, even if buffering against risk for alcohol problems (see Johnson et al., 2009; Müller et al., 2013). Such contextual factors may affect relationships between childhood cognitive performance and subsequent alcohol involvement; thus, examining these associations in novel societies such as Mauritius will help determine the generalizability of the developmental models that have been generated using data primarily from Western societies (see Luczak et al., 2014). Given our prior findings with this sample that found gender and an index of psychosocial adversity (based on familial, housing, and environmental variables) were associated with IQ (Liu, Raine, Venables, & Mednick, 2004; Lynn, Raine, Venables, Mednick, & Irwing, 2005), and that being Muslim was protective for lifetime drinking but not for alcohol problems among drinkers (Luczak et al., 2014) we recognized the importance of including gender, psychosocial adversity, and Muslim religion when examining the link between cognition and alcohol use in this sample.
MethodData were from the Joint Child Health Project (JCHP), a longitudinal study in Mauritius that has followed a birth cohort of 1,795 children since 1972 when they were 3 years old (see Raine, Liu, Venables, Mednick, & Dalais, 2010). The original sample was 51% male and, similar to the population of the island, 69% were of Indian heritage, 26% Creole (admixture of primarily African descent), and 6% other (primarily of Chinese and French heritage); 17% were Muslim.
Childhood Data Collection Phase
At age 11 years, participants were assessed on cognitive ability and home environment (see each scale for n values, which differ across scales because a cyclone brought this testing phase to an early end). The 11-year-old sample did not differ significantly from the 3-year-old sample on gender, ethnicity, or psychosocial adversity (see Raine, Reynolds, Venables, Mednick, & Farrington, 1998).
All instruments were administered to children individually by trained research staff. The official language of Mauritius is English, and schooling is conducted primarily in English; but because the common spoken language on the island is Kreol, instructions were given in Kreol. Seven subtests of the Wechsler Intelligence Scale for Children (WISC; Wechsler, 1949) were administered to 1,258 children. These subtests were used to create estimates of Performance IQ (PIQ; Picture Completion, Block Design, Object Assembly, Coding, and Mazes) and Verbal IQ (VIQ; Similarities and Digit Span). Scores were standardized and normalized within the sample (see Lynn et al., 2005 and Raine, Yaralian, Reynolds, Venables, & Mednick, 2002, for details).
The Trail Making Test (TMT; U.S. Army, 1944) was administered to 1,157 of the children. The two components of this task (Parts A [TMT-A] and B [TMT-B]) assess visuomotor tracking, motor speed, and attention, and TMT-B also contains a working memory component requiring mental flexibility (Lezak, 1995; Reitan, 1958). The difference score for TMT-B versus TMT-A (TMT B-A), an indicator of complex divided attention and sequencing (Strauss, Sherman, & Spreen, 2006), was calculated and corrected for age (M = 11.1 years, SD = 0.70) by residualization (see Raine, Reynolds, Venables, & Mednick, 2002).
At the end of primary school (sixth year of school), students take the Certificate of Primary Education (CPE) achievement examination that covers a range of academic topics (English, French, Mathematics, Environmental Studies; see SACMEQ, 2012). Unweighted scores on the CPE range from 0−20 and represent an overall index of academic achievement. CPE scores were obtained from official records for 1,415 of the sample.
Midadulthood Data Collection Phase
In mid–adulthood (M = 36.9 years, SD = 1.39), all available participants (n = 1,209 [67%]) were assessed for lifetime alcohol use and problems (other 9% abroad, 4% refused, 2% deceased, and 18% unable to contact). Written informed consent was obtained, and the research was approved by the University of Southern California Institutional Review Board. The sample assessed in adulthood did not differ significantly from the 3-year-old sample on ethnicity or psychosocial adversity, but was more likely to be male (55% vs. 51%; see Luczak et al., 2014).
Trained research staff interviewed participants about their drinking histories in Kreol. Lifetime drinkers were defined as those who had ever consumed at least one standard drink (about 14 g of alcohol), and the age when the first standard drink was consumed was obtained. Lifetime drinkers were assessed for lifetime Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV) AUD symptoms (American Psychiatric Association, 1994) using the Structured Clinical Interview for DSM–IV Diagnosis (Spitzer, Gibbon, & Williams, 1997. Only 15 (1.2%) participants reported they had consumed a standard drink and none (0%) endorsed having an AUD symptom as of age 11 years.
Final Analytic Sample
We removed four participants who were developmentally delayed, resulting in a final analytic sample of 1,107 with childhood cognitive and adult alcohol data (see Table 1). This sample was 72% Indian, 21% Creole, and 7% other; 55% were male and 22% Muslim. Scores on the cognitive measures and psychosocial adversity did not differ significantly from the full sample assessed at age 11 years. Lifetime drinker (66%) and AUD (16%) prevalence were similar to those previously reported for the full sample assessed in midadulthood (67% and 16%, respectively; Luczak et al., 2014). In those who endorsed an AUD symptom (n = 205), mean symptom count was 3.7 (SD = 2.46; range: 1–11).
Intercorrelations Among Cognitive Measures and Demographic Covariates
Data Analyses
We ran logistic regression models to examine cognitive predictors and covariates of being a lifetime drinker. We used zero-inflated negative binomial (ZINB) regression models to examine cognitive predictors and demographic covariates of AUD symptom count. ZINB is a two-part parametric mixture model for count data that have a large proportion of zero values and a highly skewed distribution of nonzero values, as is typically found for AUD symptoms in general populations samples (see Pardini, White, & Stouthamer-Loeber, 2007). ZINB models are also appropriate when there is heteroscedasticity in the count, which may occur if one covariate group (e.g., males, Muslims) produces different counts than another (Neelon & O’Malley, 2014).
We first modeled each cognitive predictor alone, then with demographic covariates (i.e., gender, childhood psychosocial adversity, and Muslim religion). All predictor variables were normally distributed (skew < |.90|, all kurtosis < |1.76|; note that we divided IQ variables by 10 to put all predictors on similar scales to yield more interpretable betas). We ran a final set of models that simultaneously entered the cognitive measures that were significant individual predictors of AUD symptoms to examine these cognitive variables in concert. Models with significant quadratic or interaction terms (created through cross-multiplication based on centered predictors) are presented only when one of these terms was significant.
ResultsTable 1 shows basic associations among the predictors in our models. Consistent with our prior publications (Liu et al., 2004; Lynn et al., 2005; Yarnell et al., 2013), being male correlated with higher PIQ, childhood psychosocial adversity correlated with poorer cognitive performance, and the three demographic variables did not correlate with one another.
Predictors of Lifetime Drinking
Logistic regression models found that those performing better on the TMT B-A were more likely to be lifetime drinkers, even after the addition of covariates in the model (b = −2.48, p = .017). A significant association of PIQ (b = 0.14, p = .005) with lifetime drinking in the univariate model was reduced to nonsignificant with the addition of covariates (p = .73). Neither VIQ nor CPE scores were significantly associated with lifetime drinking, with or without covariates.
Predictors of AUD Symptoms
Table 2 shows results of the symptom count portion of the ZINB models for each cognitive predictor alone and with the three demographic covariates.
Childhood Cognitive Predictors of Lifetime Alcohol Use Disorder Symptom Count With and Without Covarying for Gender, Childhood Psychosocial Adversity, and Muslim Religion
Intellectual ability
In univariate models, lower scores on each IQ scale predicted AUD symptoms (ps < .04), but only VIQ remained a significant predictor with covariates (PIQ reduced to p = .09). We found one significant VIQ × Male interaction (b = 0.05, p = .017), which we probed by rerunning the model separately for each gender. Lower VIQ was a stronger predictor of AUD symptoms for females (b = −0.53, p < .001) than for males (b = −0.08, p = .07).
Trail Making Test
Models including nonlinear terms revealed a significant negative effect of (TMT B-A)2 on AUD symptoms (b = 0.07, p < .05). We probed this effect by reestimating the model with the TMT B-A distribution trichotomized into three groups (fast = < −1 SD; midrange = between −1 and +1 SD; slow = > 1 SD). The relationship of TMT B-A with AUD symptom count differed in both direction and magnitude across the three groups (fast b = −1.13, p = .79; midrange b = 0.94, p = .55; and slow b = 2.61, p = .05). Only for those in the slow range was there indication of TMT B-A being predictive of AUD symptoms.
Academic achievement
Lower CPE scores were predictive of higher AUD symptoms, with (p = .029) and without (p = .012) covariates included in the model.
Multiple cognitive predictors
Lastly, we entered the two cognitive variables that were significant in linear models, VIQ and CPE scores, simultaneously in ZINB models with and without covariates. When entered together, neither predicted AUD symptoms (ps > .24), indicating the portion of each of these associated with AUD symptoms may be shared.
DiscussionThis study examined cognitive abilities and academic achievement in a general cohort, non-Western sample of 11-year-old youth as predictors of alcohol involvement over approximately the next 25 years. Lifetime drinking was predicted only by better childhood performance on the TMT B-A once demographic covariates were taken into account. Lower verbal abilities and academic achievement were linearly associated with subsequent alcohol problems, whereas the relationship between poorer performance on the TMT B-A and alcohol problems only emerged at the lower end of the performance range. All of these associations were found prospectively in a general sample of youth tested prior to the typical onset of drinking in this society (99% of the sample had not consumed a full drink), indicating these associations existed prior to alcohol use and were not merely consequences of consumption.
Being a lifetime drinker was not strongly linked to childhood cognitive measures in this Mauritian sample after accounting for demographic covariates. Studies of twin samples have found that initiation of alcohol use is more strongly influenced by environmental factors than genetic factors (e.g., Heath, Meyer, Eaves, & Martin, 1991; Rhee et al., 2003), and thus factors associated with lifetime drinking status may vary more across cultures. One environmental explanation that has been proposed for the higher levels of drinking seen among those with higher intellectual ability and achievement in Western societies is that this increases the opportunity for alcohol use (e.g., Johnson et al., 2009; Slutske et al., 2004). On Mauritius, however, higher academic achievement does not typically place young adults in a more risky drinking environment, given that higher education is not linked to moving away from one’s family of origin, nor does high academic achievement necessarily allow one to pursue higher education. Thus, our findings are consistent with the idea that exposure to drinking environments contributes to the link between childhood cognitive performance and lifetime drinking. Additional examination of differences in drinking norms, contexts, and accessibility across societies may further elucidate unique environmental components of this relationship.
On the other hand, both childhood verbal intelligence and academic achievement were predictive of lifetime alcohol-related problems, even after accounting for gender, childhood psychosocial adversity, and Muslim religion in this Mauritian sample. These results are consistent with other prospective cohort studies in Western societies (e.g., Batty et al., 2006; Windle & Blane, 1989), providing further evidence that poor cognitive performance in childhood is a robust predictor of later alcohol problems across cultures.
In our sample, verbal intelligence was a stronger predictor of lifetime alcohol problems in females than in males. The specificity of the relationship of lower verbal abilities with alcohol problems in our female sample suggests the possibility of a pathway to alcohol problems that is distinct from the deviance proneness pathway (which is more associated with behavioral undercontrol in males; Sher, 1991) that may operate more through other factors such as social skills and judgment (see Maggs, Patrick, & Feinstein, 2008; Windle & Blane, 1989). Further examination of gender differences in models that include social, peer, and situational factors may help explain the association between verbal abilities and alcohol-related problems.
Poor academic achievement in childhood was found to be a significant predictor of lifetime alcohol problems in both males and females. This relationship remained significant after covarying for psychosocial adversity, even though previous reports with this sample have shown psychosocial adversity to be associated with lower IQ scores (Liu et al., 2004). In Mauritius, primary education is mandated, and the children in our cohort had relatively uniform educational opportunities up through age 11 years, regardless of psychosocial adversity. A mediational pathway cannot be established with these data, but our findings indicate that poor academic achievement by the end of primary school is a risk factor for subsequent alcohol problems that is not simply accounted for by concurrent psychosocial adversity.
The measure of TMT B-A exhibited a curvilinear relationship with lifetime alcohol problems. More severe deficits served as a risk factor, but stronger performance did not add any protection against developing alcohol problems. Our findings suggest that, within a general sample, the link between poor functioning on the TMT B-A in childhood and subsequent alcohol problems emerges only for those in the lowest performance range. The deviance proneness model hypothesizes that underlying behavioral dysregulation manifests in childhood as attention and cognitive deficits; the TMT B-A difference score, a measure of complex divided attention and sequencing (Strauss et al., 2006), may be assessing abilities that are indicators of this dysregulation construct. Future research examining general samples that include individuals on the tail ends of performance will improve our understanding of the specificity of various cognitive measures as precursors to alcohol problems as well as their relationships with other measures of behavioral dysregulation.
Our findings should be interpreted along with the limitations of the study, including the use of a limited number of measures to assess cognitive performance in a culture for which they were not designed, attrition as is the case in any longitudinal study, and the inability to speak to mechanisms for how cognitive deficits in childhood lead to alcohol problems by adulthood. Despite these limitations, this study provides evidence that childhood cognitive performance is not strong a predictor of lifetime alcohol use in this society, indicating environmental specificity of previously found relationships in Western societies, but that childhood cognitive deficits are risk factors for subsequent alcohol problems in this novel east African cultural context, providing further evidence of the generalizability of this relationship across societies.
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Submitted: July 10, 2014 Revised: September 30, 2014 Accepted: October 6, 2014
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Record: 14- Childhood family characteristics and prescription drug misuse in a national sample of Latino adults. Vaughan, Ellen L.; Waldron, Mary; de Dios, Marcel A.; Richter, James; Cano, Miguel Ángel; Psychology of Addictive Behaviors, Vol 31(5), Aug, 2017 pp. 570-575. Publisher: American Psychological Association; [Journal Article] Abstract: Prescription drug misuse is a growing public health concern and has been understudied in Latino populations. The current study tests the relationships between childhood and family characteristics and prescriptions drug misuse among adult Latinos. A subsample of 8,308 Latinos from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were examined. Logistic regression analyses tested associations between parental alcoholism, parental divorce before age 18, and parental death before age 18 and prescription drug misuse and prescription drug use disorder. Parental alcoholism and parental divorce increased the odds of both prescription drug misuse and use disorder. Parental death increased the odds of prescription drug use disorders. The results have important implications for understanding the complex associations between family psychosocial history and prescription drug misuse. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Childhood Family Characteristics and Prescription Drug Misuse in a National Sample of Latino Adults / BRIEF REPORT
By: Ellen L. Vaughan
Department of Counseling and Educational Psychology, Indiana University Bloomington;
Mary Waldron
Department of Counseling and Educational Psychology, Indiana University Bloomington
Marcel A. de Dios
Department of Health Disparities Research, University of Texas M. D. Anderson Cancer Center
James Richter
Department of Counseling and Educational Psychology, Indiana University Bloomington
Miguel Ángel Cano
Department of Epidemiology, Robert Stempel College of Public Health & Social Work, Florida International University
Acknowledgement: A poster using the broader National Epidemiologic Survey on Alcohol and Related Conditions data looking at associations between parental divorce and death and prescription drug misuse was presented at the American Psychological Association in 2010. The results of the current study focusing on the Latino subsample have not been previously disseminated.
Note: John M. Roll served as the action editor for this article.
The Centers for Disease Control and Prevention ([CDC]; 2016) report that overdoses from prescription drugs resulting in death increased rapidly from 1999 to 2014. The CDC analyzed the morbidity underlying these overdoses and found that number of emergency room visits for nonmedical use of opioid analgesics increased by 111% between 2004 and 2008 (Cai, Crane, Poneleit, & Paulozzi, 2010). Emergency room visits related to nonmedical benzodiazepine use increased 89% from 2004 to 2008. The misuse of prescription drugs is a public health problem that permeates the range of communities from urban cities to rural communities (Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2013). Data from the National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration [SAMHSA], 2015) indicate that approximately 6.5 million (2.5%) Americans use psychotherapeutic drugs for nonmedical use each month.
Research investigating prescription drug misuse among Latino populations is sparse and needed. Latinos represent a large and growing population in the United States (Stepler & Brown, 2016). It has been noted that because the population is young, Latinos are at increased risk for substance use (Volkow, 2006). Among Latino eighth graders, the rates of use for various prescription drugs such as hydrocodone are on par with or exceed use of these substances by non-Latino, White youth (Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2016). The rates of use fall by 12th grade, but this may be due to high rates of dropout and school exclusion by minority youth. In adulthood and older adulthood, the rates of prescription drug misuse among Latinos is also concerning (SAMHSA, 2015). For example, a recent study showed that, among adults age 65 and older, Latinos having a greater odds of misusing prescription drugs than non-Latino Whites or African Americans (Moore et al., 2009).
The empirical literature addressing correlates of substance use among Latinos has often focused on important cultural and contextual factors. Familismo, which reflects the cultural saliency of family for Latinos, remains a powerful cultural value for Latinos (Santiago-Rivera, Arredondo, & Gallardo-Cooper, 2002). Thus, prevention and intervention efforts with Latinos are often family focused (e.g., Familias Unidas; Coatsworth, Pantin, & Szapocznik, 2002). Family factors have been related to substance use among Latino adolescents and emerging adults (Knight et al., 2010). There are a number of studies linking illicit substance use, including nonprescribed use of prescription drugs, to parental alcoholism and parental divorce in predominantly White, non-Latino samples (e.g., Waldron, Grant, et al., 2014, Waldron, Vaughan, et al., 2014). However, little is known about parental alcoholism and parental divorce before the age of 18 in Latino families and their relationship to prescription drug misuse, specifically opiates, tranquilizers, and benzodiazepines. To our knowledge, there are no published studies of parental death before the age of 18 and risk of prescription drug misuse, regardless of racial/ethnic background.
Research testing associations between family characteristics and prescription drug misuse among Latinos has had some growth, but has largely focused on adolescents. For example, Ford and Rigg (2015) found that stronger bonds with parents reduced the odds of prescription pain reliever misuse among Latino adolescents. In another study, perceived parental disapproval of marijuana and alcohol use decreased the odds of prescription drug misuse among Latinos (Conn & Marks, 2014). Two studies report associations with family structure, including parental separation or divorce. Using Monitoring the Future data, Harrell and Broman (2009) did not find an association between family structure (e.g., single parenthood and stepfamily vs. two-parent household) and prescription drug misuse among Latino adolescents. Barrett and Turner (2006) found that less problematic adolescent substance use was associated with intact mother-father families than single-parent families. However, those single-parent families that had at least one additional relative living in the household did not show the increased risk for substance use among adolescents. While this study had large subsample of Latino youth, the authors did not assess these specific relationships stratified by racial or ethnic groups.
Parental alcoholism has not been studied in relation to prescription drug misuse among Latino samples. Such an omission is surprising given long-documented research on children of alcoholics and their increased risk for early and problem involvement across a range of substance classes (Sher, 1991; Windle & Searles, 1990), including prescription drugs (Tucker et al., 2015). Information on parental alcoholism and both parental divorce and parental death during childhood is important to understanding the associations between family context and prescription drug misuse in an understudied sample of Latinos. In addition, the current study will also include nativity as an important covariate for Latino populations. Previous research has indicated that Latinos born outside of the United States have lower rates of substance use disorders (Alegria, Canino, Stinson, & Grant, 2006). The aim of the current study is to test association between family characteristics prior to age 18 and prescription drug misuse in a national sample of Latino adults. Important demographic covariates such as biological sex and nativity will be included in models testing these associations.
MethodThis study is a secondary data analysis of data from wave one of the NESARC. The NESARC is a representative sample of noninstitutionalized adults in the United States and District of Columbia (Chen et al., 2006; Grant & Dawson, 2006). Participants’ data are gathered through in-home computer assisted interviews. The primary goal of NESARC is to ascertain the prevalence of alcohol use disorders and related problems. Data are collected on a broad spectrum of substance use behaviors including use of prescription drugs without a prescription. Review by the institutional review board determined that the research was exempt from review due to the publically available and deidentified nature of the data.
Participants
Participants for this secondary data analysis were self-identified Latino adults who participated in wave one of the NESARC (n = 8,308). The sample was nearly evenly split between men and women (49.1% female) and over half of the sample was born outside of the United States (57.3%). With respect to prescription drug misuse, 4.9% of the sample reported lifetime prescription drug misuse and 1.4% reported lifetime prescription drug use disorder. More detailed demographic information can be found in Table 1.
Frequencies
Measures
Demographic variables
Demographic variables included biological sex, marital status, income, and nativity. Biological sex (female = 0; male = 1), marital status (not married = 0; married = 1), and nativity (0 = born outside of the U.S.; 1 = born in the U.S.) were all dummy coded. Personal income was a four-level categorical variable representing, 0 = $0–$19,999, 1 = $20,000–$49,999, 2 = $50,000–$79,999, and 3 = $80,000 and up.
Childhood family characteristics
Participants were asked whether they had experienced a number of family transitions prior to the age of 18. For the current study, we tested whether participants experienced divorce of a parent (0 = no, 1 = yes) and whether the participant experienced the death of a parent (0 = no, 1 = yes).
Parental alcoholism
Two items assessed maternal and paternal alcoholism. Participants were asked, “Has your blood or natural <parent> been an alcoholic or problem drinker at any time in <his/her> life?”. These two items were combined and coded to indicate whether or not the participant’s parent had parental a history of alcoholism (0 = no, 1 = yes).
Substance use disorders
Alcohol use disorder was coded as lifetime history of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) alcohol abuse or dependence (0 = no, 1 = yes). Nicotine dependence is based upon DSM–IV criteria and reflects lifetime nicotine dependence (0 = no, 1 = yes).
Prescription drug misuse and disorder
Lifetime prescription drug misuse was coded using three items, whether the participant had ever used (a) sedatives, (b) tranquilizers, or (c) opioids (heroin is asked separately; [0 = no, 1 = yes]). Participants were instructed to respond about use that was “on your own-that is either without a doctor’s prescription; in greater amounts, more often or longer than prescribed; or for a reason other than a doctor said you should use them.” Prescription drug use disorder was coded as lifetime history of DSM-IV sedative, tranquilizer, or opioid (excluding heroin) abuse or dependence (0 = no, 1 = yes).
Data Analytic Plan
Preliminary descriptive analyses and logistic regression analyses were conducted for the current study. In order to account for the sampling design, the complex samples module for SPSS 24 was used for data analysis. First, a series of unadjusted analyses were conducted to test bivariate associations between each family variable of interest and covariates with prescription drug misuse. The same unadjusted analyses were conducted to test bivariate associations between each family variable of interest and covariates with prescription drug use disorders. Next, an overall logistic regression model was tested using all significant variables from unadjusted bivariate analyses; in these analyses, conducted separately for prescription drug misuse and prescription drug disorders, the following independent variables were modeled: biological sex, income, nativity, marital status, parental alcoholism, parental divorce before age 18, and parental death before age 18.
ResultsFor lifetime prescription drug misuse, unadjusted analyses were statistically significant for all variables with the exception of parental death before age 18 (Table 2). In adjusted analyses, the overall model was statistically significant, Wald F = 578.75, p < .001 and explained between 5.7% (Cox and Snell) and 17.7% (Nagelkerke) of the variance in prescription drug misuse. Parental alcoholism (odds ratio [OR] = 1.19, 95% CI [1.07, 1.32]) and parental divorce (OR = 1.55, 95% CI [1.41, 1.71]) before the age of 18 both increased the odds of prescription drug misuse. Lifetime alcohol use disorder (OR = 3.85, 95% CI [3.46, 4.29]) and nicotine dependence (OR = 3.48, 95% CI [2.91, 4.20]) were both associated with greater odds of prescription drug misuse. In adjusted analyses, women had greater odds of prescription drug misuse (OR = 1.25, 95% CI [1.15, 1.37]). Participants born outside of the United States (OR = .66, 95% CI [.58, .74]) and those married or living as married (OR = .68, 95% CI [.60, .76]) had lower odds of prescription drug misuse. Compared with those with incomes greater than $80,000, those with lower income had reduced odds of prescription drug misuse (see Table 2 for ORs by income group).
Unadjusted and Adjusted Odds Ratios
The next series of analyses tested the associations between the family variables of interest and prescription drug use disorder. All variables were significantly related to the outcome and, thus, were retained in adjusted analyses. The overall model was statistically significant, Wald F = 1,873.24, p < .001 and explained between 3.4 (Cox and Snell) and 26.4% (Nagelkerke) of the variance in prescription drug use disorder. Parental alcoholism (OR = 1.35, 95% CI [1.07, 1.71]), parental divorce (OR = 2.43, 95% CI [1.96, 2.99]), and death of a parent (OR = 1.63, 95% CI [1.22, 2.19]) all increased the odds of a prescription drug use disorder. Lifetime alcohol use disorder (OR = 5.92, 95% CI [5.26, 6.67]) and nicotine dependence (OR = 4.57, 95% CI [3.87, 5.46] were both associated with greater odds of prescription drug misuse. Women (OR = .77, 95% CI [.64, .92]), participants born outside of the United States (OR = .52, 95% CI [.44, .62]), and those married or living as married (OR = .53, 95% CI [.41, .68]) had lower odds of a prescriptions drug use disorder. Like lifetime prescription drug misuse, those with lower incomes had lower odds of prescription drug use disorder when compared with those with incomes greater than $80, 000 (see Table 2 for odds ratios by income group).
DiscussionThe aim of the current study was to test the associations between family characteristics prior to age 18 and prescription drug misuse and prescription drug use disorder in a national sample of Latino adults. Prescription drug misuse is a critical public health problem and, historically, has been studied less than other substance classes such as alcohol, tobacco, and marijuana. Understanding risk and protective factors for prescription drug use is critical for the development of prevention and intervention for Latino populations. Family is a core developmental context and Familismo represents an important Latino cultural value (Santiago-Rivera et al., 2002). With respect to prescription drug misuse, it is not clear whether family variables are related in the same ways as for other substances given that attitudes regarding prescription drug use may be more permissive than for other drugs.
Results of the current study underscore the role that family disruption may play in prescription drug misuse behaviors. Parental alcoholism and parental divorce before the age of 18 both increased the odds of any prescription drug misuse as well as prescription drug use disorder even when accounting for other substance use disorders. These results are inconsistent with the adolescent literature which found no relationship between family structure and prescription drug misuse (Harrell & Broman, 2009). There are a number of reasons why this may have occurred. The NESARC is a study of adults, and thus, the prevalence of prescription drug use is greater and allowed for such associations to emerge later in participants’ lives, when access to prescription drugs may be more likely. Barrett and Turner (2006) found that intact family structure was associated with reduced risk for substance use among a sample of young adults with a large subsample of Latinos. The results of the current study are consistent with their study. With respect to parental alcoholism, the results are consistent with other research that has found broad associations between parental alcoholism and substance use disorders (Mellentin et al., 2016) and parental substance use and adolescent prescription drug misuse (Tucker et al., 2015). Death of a parent before the age of 18 increased the odds of a prescription drug use disorder. This result is consistent with previous research using a Caucasian sample of male twins that found associations between parental death and the development of alcohol use disorders (Otowa, York, Gardner, Kendler, & Hettema, 2014).
Of note, a number of control variables were also significantly related to prescription drug misuse and prescription drug use disorders in this Latino subsample. Alcohol and nicotine use disorders were robust predictors of prescription drug misuse. This finding is not surprising, given high rates of co-occurring substance use and substance use disorders (Falk, Yi, & Hiller-Sturmhöfel, 2006, 2008). Latinas were at lower odds for lifetime prescription drug disorders, but not lifetime misuse. Consistent with previous research using the NESARC that found a lower lifetime prevalence of substance use disorders among those born outside the United States (Alegria et al., 2006), participants who were born outside of the United States were also at lower odds for prescription drug misuse and disorders. One potential explanation is that those who are born outside of the United States may hold traditional values that do not promote substance use.
Participants with lower incomes also had lower odds of prescription drug misuse and disorder. More specifically, compared with those participants who reported family incomes greater than $80,000, those in every other income category had lower odds of prescription drug misuse and prescription drug misuse disorders. These results are contrary to other NESARC studies that include all racial and ethnic groups (e.g., Martins, Keyes, Storr, Zhu, & Chilcoat, 2009; Wu, Woody, Yang, & Blazer, 2010). This finding may be connected to the focus on Latino participants in the study. Lower incomes may be a proxy indicator for less acculturation to dominant United States culture which has been associated with lower rates of substance use among Latinos (Zemore, 2007). Further, while not the case for other racial and ethnic groups and prescription drug access, lower income for Latino groups may be associated with poorer access to both health care and the financial means to obtain such substances. This represents an important area of further research. Finally, consistent with previous research, participants who are married or living as married had lower odds of prescription drug misuse and disorder than those with other marital statuses. It may be that being married or in a marriagelike relationship confers a greater level of social support and connectedness to reduce risk for prescription drug misuse.
The current study should be understood in the context of its strengths and limitations. First, the NESARC is a national representative sample allowing for greater generalizability of findings to the larger Latino population in the United States. This is important, given that many studies of Latinos are from geographically restricted areas. Second, there has been a growing interest in prescription drug misuse and research is needed to identify important risk and protective factors among understudied populations such as the Latino population. The aim of the current study was to focus in on family factors as an important developmental and cultural context for this population and to tests these variables with respect to prescription drug misuse and disorder in a population of adults.
There are limitations of the current study also worth noting. The results are correlational in nature and do not allow for causal conclusions. Family factors account for a small amount of the variance in prescription drug misuse. That said, this is not atypical in risk and protective factor research for substance use and use disorders. The factors that contribute to prescription drug use come from multiple and often related contexts. The aim of the current study was to test a group of variables (family characteristics) that are culturally relevant for Latinos to inform prevention and intervention. In addition, the current study was not able to test other cultural variables such as acculturation and enculturation as the NESARC does not have such variables. Due to low prevalence, particularly for the prescription drug use disorder, the results of the current study did not allow for analyses for separate drug classes used in the composite variables.
Future research in this area should look at childhood family factors using longitudinal methods as well as test important mediators and moderators of these relationships. In addition, the roles that culture and cultural values play in substance use behaviors is relevant to Latinos living in the United States. Current models of acculturation are tuned into behavioral and values shifts as well as the receiving environment for Latino populations born outside of the United States (Schwartz, Unger, Zamboanga, & Szapocznik, 2010). These cultural contexts are influential in drug use attitudes and behaviors and are an important area of further research with respect to prescription drug misuse. For example, future research might test how family structure (including parental divorce) might moderate the relationship between the Latino cultural value of Familismo and prescription drug misuse. In sum, the relationships between family structure, family related values and substance use behaviors among Latinos are complex and warrant further research to inform the development of prevention and intervention strategies.
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Submitted: October 19, 2016 Revised: March 6, 2017 Accepted: March 19, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (5), Aug, 2017 pp. 570-575)
Accession Number: 2017-17890-001
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Record: 15- Coping-motivated marijuana use correlates with DSM-5 cannabis use disorder and psychological distress among emerging adults. Moitra, Ethan; Christopher, Paul P.; Anderson, Bradley J.; Stein, Michael D.; Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015 Special Section: Marijuana Legalization: Emerging Research on Use, Health, and Treatment. pp. 627-632. Publisher: American Psychological Association; [Journal Article] Abstract: Compared to other age cohorts, emerging adults, ages 18–25 years, have the highest rates of marijuana (MJ) use. We examined the relationship of using MJ to cope with negative emotions, relative to using MJ for enhancement or social purposes, to MJ-associated problems and psychological distress among emerging adults. Participants were 288 community-dwelling emerging adults who reported current MJ use as part of a 'Health Behaviors' study. Linear and logistic regressions were used to evaluate the adjusted association of coping-motivated MJ use with the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) cannabis use disorder, MJ-related problem severity, depressive symptoms, and perceived stress. After adjusting for other variables in the regression model, using MJ to cope was positively associated with having DSM-5 cannabis use disorder (OR = 1.85, 95% CI [1.31, 2.62], p < .01), MJ problem severity (b = .41, 95% CI [.24, .57], p < .01), depression (b = .36, 95% CI [.23, .49], p < .01), and perceived stress (b = .37, 95% CI [.22, .51], p < .01). Using MJ for enhancement purposes or for social reasons was not associated significantly with any of the dependent variables. Using MJ to cope with negative emotions in emerging adults is associated with MJ-related problems and psychological distress. Assessment of MJ use motivation may be clinically important among emerging adults. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Coping-Motivated Marijuana Use Correlates With DSM-5 Cannabis Use Disorder and Psychological Distress Among Emerging Adults
By: Ethan Moitra
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University;
Paul P. Christopher
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University and Butler Hospital, Providence, Rhode Island
Bradley J. Anderson
General Medicine Research Unit, Butler Hospital
Michael D. Stein
Department of Medicine and Department of Health Services, Policy, and Practice, Warren Alpert Medical School of Brown University and Butler Hospital
Acknowledgement: This study was funded by NIAAA Grant R01 AA020509. Trial registered at clinicaltrials.gov as NCT01473719.
Evolving social attitudes toward marijuana (MJ) have led to legalization of its use for medical and recreational purposes in some U.S. states. Over the past 30 years, disapproval of MJ use has decreased across birth cohorts (Keyes et al., 2011), with emerging adults (ages 18–25 years) being most accepting of use. Compared to other age cohorts, emerging adults (Arnett, 2001) also have the highest rates of MJ use, and substance use disorders peak during this period (Substance Abuse and Mental Health Services Administration [SAMHSA], 2013). Indeed, studies have shown that nearly 10% of college-based emerging adults meet criteria for an MJ use disorder (Caldeira, Arria, O’Grady, Vincent, & Wish, 2008; Caldeira et al., 2009).
Reasons for substance use can vary between and within individuals (Cooper, 1994). Common motives are to cope with negative emotions or distress (e.g., “to forget my worries”), to conform (e.g., “because I felt pressure from others who do it”), for enhancement purposes (e.g., “because I like the feeling”), for expansion (e.g., “to expand my awareness”), and for social purposes (e.g., “it’s what I do with friends”). Conformity-motivated use is driven by a desire to reduce social exclusion, enhancement-motivated use is described as being driven by a desire for excitement or joy, expansion-motivated use relates to seeking cognitive or perceptual enhancement of experiences, and social-motivated use seeks to facilitate social cohesion (Simons, Correia, Carey, & Borsari, 1998; Simons, Gaher, Correia, Hansen, & Christopher, 2005). According to the stress-coping model (Wills & Shiffman, 1985), people may also consume substances as a coping response to stress, with the substance being used to engender positive affect and/or decrease an aversive mood.
Enhancement, expansion, and social motives are positively associated with MJ use but less related to negative outcomes such as MJ-related problems or psychological distress (Bonn-Miller, Zvolensky, & Bernstein, 2007; Brodbeck, Matter, Page, & Moggi, 2007). However, individuals with MJ use disorders have higher rates of enhancement-motivated MJ use compared to more casual MJ users (Bonn-Miller & Zvolensky, 2009). Although using MJ to conform is associated with social anxiety symptoms (Buckner, Bonn-Miller, Zvolensky, & Schmidt, 2007), it has been found to negatively correlate with recent MJ use (Bonn-Miller, Zvolensky, & Bernstein, 2007).
Although most emerging adults report using MJ primarily for enhancement or social reasons (Lee, Neighbors, & Woods, 2007), those who endorse greater MJ use to cope with distress may represent a subgroup trying to manage more severe mental health problems and, in doing so, may be at risk for MJ-related problems. Emerging adults may be more likely than other adults to use MJ to cope with psychological distress (Buckner, 2013). Using MJ to cope with distress is associated with negative affect, anxious arousal, and depressive symptoms (Beck et al., 2009; Mitchell, Zvolensky, Marshall, Bonn-Miller, & Vujanovic, 2007). Among emerging adults with a history of trauma, coping-motivated use, but not other motives, is associated with posttraumatic stress symptoms (Bonn-Miller, Vujanovic, Feldner, Bernstein, & Zvolensky, 2007). Using MJ to cope with negative emotions is also uniquely associated with emotional dysregulation (Bonn-Miller, Vujanovic, & Zvolensky, 2008) and social anxiety symptoms (Buckner et al., 2007) relative to other motives. Although these studies indicate that emerging adults who use MJ to cope experience psychological distress, they are limited by the exclusion of individuals with current Axis I psychopathology (Bonn-Miller, Vujanovic, et al., 2007) and restriction to college students (Buckner et al., 2007). A more representative sample of emerging adults who use MJ to cope with distress is needed to better understand the relationship among these factors.
Coping-motivated MJ use in emerging adults is also associated with more persistent use (Patrick, Schulenberg, O’Malley, Johnston, & Bachman, 2011; Patrick, Schulenberg, O’Malley, Maggs, et al., 2011; Titus, Godley, & White, 2007). Persistent use can lead to MJ-related problems, particularly for individuals who start using earlier in life (Anthony & Petronis, 1995). Among emerging adult MJ users, those who use to cope with distress are at increased risk for MJ-related problems (Buckner, 2013; Lee et al., 2007) and are more likely to meet criteria from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 2000) for MJ dependence (Bonn-Miller & Zvolensky, 2009). Yet the DSM-IV had inadequate clinical utility in discriminating MJ problem severity among emerging adults (Martin, Chung, Kirisci, & Langenbucher, 2006). To our knowledge, no study has investigated the link between using MJ to cope with distress and cannabis use disorder as defined in the fifth edition of the DSM (DSM-5; American Psychiatric Association, 2013).
In this study, we examined the association of motivations for using MJ (social, enhancement, and coping) in emerging adults with four measures of MJ-related problem severity and psychological distress: (a) meeting DSM-5 criteria for cannabis use disorder, (b) MJ-related problem severity, (c) depressive symptomatology, and (d) perceived stress. We hypothesized that coping-motivated use would be more strongly associated with these adverse outcomes than social- or enhancement-motivated use.
Method Participants
Participants were recruited for a large study on health behaviors among emerging adults who use MJ or alcohol through advertisements online, in local college newspapers, on public transportation, and on commercial radio in Rhode Island. After a telephone screen, eligible individuals were invited for a compensated ($40) in-person interview and free sexually transmitted infection testing. The study was approved by the Butler Hospital Institutional Review Board.
Eligibility criteria included being 18–25 years old, drinking alcohol and/or using MJ in the past month, being sexually active in the past 6 months, not having suicidal ideation in the past 2 weeks, and living within 30 minutes of the research site. Of the 1,621 individuals screened by phone, 689 were ineligible. The remaining 932 eligible persons were invited for an interview, and 533 were either not interested or did not keep a scheduled baseline appointment. In total, 399 individuals completed baseline interviews, after which 17 persons were found to be ineligible. For the present analysis, we included data only from individuals who reported using MJ in the past 30 days (n = 288).
Measures
Frequency of cigarette smoking
Frequency of cigarette smoking was assessed using an item from the Fagerström Test for Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerström, 1991): “How many cigarettes do you smoke per day?”
Marijuana Problem Scale
The Marijuana Problem Scale (MPS; Stephens, Roffman, & Curtin, 2000; Stephens et al., 2004) is a reliable and valid measure (score range 0–48) of 19 problems directly related to MJ use, ranging from losing a job to having withdrawal symptoms to having problems in one’s family.
Patient Health Questionnaire–9
The Patient Health Questionnaire–9 (PHQ-9; Kroenke, Spitzer, & Williams, 2001) is a validated and reliable nine-item measure (score range 0–27) of depressive symptoms.
Perceived Stress Scale–4
The Perceived Stress Scale–4 (PSS-4; Cohen, Kamarck, & Mermelstein, 1983) is a four-item measure (score range 0–16) that assesses the degree to which individuals perceive their environment and experiences as stressful.
Reasons for Marijuana Use
We adapted the Reasons for Drinking measure developed by Cooper, Russell, Skinner, and Windle (1992) for this study to examine MJ use motives. This measure has three subscales: (a) coping (sample item, “Because it helped when you felt depressed or nervous”), (b) enhancement (“Because it’s exciting”), and (c) social (“To be sociable”). The Reasons for Marijuana Use subscales had a possible range of 1–4, corresponding to never/almost never, sometimes, often, and almost always. In this sample, internal consistency reliabilities were .84, .84, and .73 for the coping, enhancement, and social scales, respectively. Product-moment correlations between the subscales ranged from .45–.47.
Structured Interview for the DSM-IV—cannabis abuse and dependence modules
The Structured Interview for the DSM-IV (First, Spitzer, Gibbon, & Williams, 1996) is the most widely used, reliable, and well-validated, structured clinical assessment tool for DSM-IV diagnostic criteria. To assess the craving criterion under DSM-5 defined cannabis use disorder, we asked all participants, “In the past 3 months, have you often had cravings or strong desires or urges to use marijuana?” Participants endorsing two or more of the abuse or dependence items, including our additional craving question, met criteria for DSM-5 cannabis use disorder. Severity of cannabis use disorder was coded by number of criteria endorsed: no disorder = zero or one symptom, mild = two or three symptoms, moderate = four or five symptoms, and severe = six or more symptoms (Hasin et al., 2013).
Timeline Follow-Back Measure
The Timeline Follow-Back Measure (TLFB; Sobell & Sobell, 1996) is a semistructured interview that uses a calendar-guided approach (Fals-Stewart, O’Farrell, Freitas, McFarlin, & Rutigliano, 2000) and assesses alcohol and MJ use in the past 30 days.
Data Analysis
Descriptive statistics summarize the characteristics of the sample. Our primary focus is on the associations of using MJ for coping, socialization, and enhancement with indicators of MJ use severity and problems. We also examined associations with measures of psychological well-being. Background characteristics included as covariates were age, gender, ethnoracial group, employment status, education, alcohol use frequency (past 30 days), MJ use frequency (past 30 days), and number of cigarettes smoked per day (past 30 days). Associations were estimated in a seemingly unrelated regression framework (Zellner, 1962) using Mplus 5.1 (Muthén & Muthén, 2008). This method assumes error terms are correlated across equations and parameter estimates are more efficient than equation-by-equation estimation. The interpretation of estimated coefficients is identical to single-equation regression models. Prior to analysis, all continuous variables were standardized to zero-mean and unit variance. For the equations with continuous dependent variables (PHQ-9, PSS-4, and MPS), the coefficients reported for continuous factors are fully standardized regression coefficients, and the coefficients for the categorical factors are y-standardized. Associations with meeting criteria for DSM-5 cannabis use disorder are reported as odds ratios. Parameters and inferential statistics were estimated using maximum likelihood with robust standard errors (MLR in Mplus). All above-described covariates and the three motivation-to-use MJ subscales were entered simultaneously in the multivariate models. An indicator variable contrasting non-Latino Whites to all other racial or ethnic identifications was used in analyses.
ResultsOf the 288 emerging adults who reported MJ use in the past 30 days, mean age was 21.2 (SD = 2.1) years, 135 (51.7%) were male, and 187 (64.9%) were non-Latino White (see Table 1). On average, participants used alcohol and MJ on 26.7% (SD = 18.0%) and 52.5% (SD = 38.1%) of TLFB days, respectively. More than two thirds (70.5%) reported no cigarette smoking in the 30 days prior to baseline.
Background Characteristics and Descriptive Statistics (n = 288)
After adjusting for other variables in the model, including using MJ for enhancement and social reasons, using MJ to cope with distress was positively and significantly associated with meeting DSM-5 diagnostic criteria for cannabis use disorder (OR = 1.85, 95% CI [1.31, 2.62], p < .01). As a supplementary analysis, we estimated a parallel ordinal logit regression model in which cannabis use disorder severity was regressed on the reasons to use indices and all covariates described in Table 2; results were consistent with those reported for the dichotomized outcome. Using MJ to cope was associated positively and significantly with cannabis use disorder severity (OR = 1.59, 95% CI [1.15, 2.19], p < .05). Neither using for social reasons (OR = 1.46, 95% CI [0.98, 2.18], p > .05) nor using for enhancement (OR = 0.94, 95% CI [0.66, 1.34], p > .05) was associated significantly with cannabis use disorder severity. Results were the same when analyzing the unique associations of the three reasons for using MJ and cannabis use disorder based on number of criteria endorsed.
Seemingly Unrelated Regression Model Estimating the Adjusted Association of Using Marijuana to Cope, to Socialize, and for Enhancement on Various Measures (n = 288)
Using MJ to cope was also significantly associated with MJ problem severity (b = .41, 95% CI [.24, .57], p < .01), depressive symptomatology (b = .36, 95% CI [.23, .49], p < .01), and perceived stress (b = .37, 95% CI [.22, .51], p < .01; see Table 2). These multivariate models estimated the effects of the other reasons for using MJ subscales, revealing that using MJ to socialize or for enhancement purposes was not uniquely associated significantly with any of the dependent variables (see Table 2).
DiscussionThis study found that among emerging adults who use MJ, use to cope with distress is positively and significantly associated with having a DSM-5 cannabis use disorder. Using MJ for enhancement or social purposes did not uniquely account for a significant proportion of variance in this outcome. Moreover, coping-motivated use, but not social- or enhancement-motivated use, is associated with MJ-related problems in this group. Using MJ to cope with negative emotions among emerging adults also appears to be uniquely associated with psychiatric symptoms, as measured by severity of depressive symptoms and degree of perceived stress, consistent with prior research (Mitchell et al., 2007). These data are the first to demonstrate the confluence of cannabis use disorder, MJ-related problems, and psychiatric symptoms in the same sample. In addition, a significant shortcoming of previous work was the exclusion of individuals who met DSM diagnostic criteria for Axis I psychopathology (Bonn-Miller, Vujanovic, et al., 2007; Bonn-Miller et al., 2008; Bonn-Miller & Zvolensky, 2009), a meaningful omission given the concern for mental health issues in these individuals.
The acceptability of MJ use is growing in emerging adults (Keyes et al., 2011), a high-risk group for substance use disorders (SAMHSA, 2013). Although prior studies have shown an association between coping-motivated MJ use and a variety of negative psychological factors (e.g., Mitchell et al., 2007), little research has compared the relationship of coping-motivated use, relative to social- and enhancement-based use, to MJ-related problems and psychological variables. These are also the first results linking using MJ to cope with psychological distress to the newly defined DSM-5 cannabis use disorder. This new diagnostic category represents an important streamlining of the DSM’s cannabis abuse and dependence diagnoses while incorporating a severity dimension. This new approach is particularly relevant to clinicians working with emerging adults, because the DSM-IV classification system poorly quantified severity of use in this age group (Martin et al., 2006).
This study had limitations. First, our primary measure of MJ use motives was adapted from an alcohol scale. Moreover, we did not measure conformity- or expansion-motivated use; these would be important to include in future research. Second, although coping-motivated use was significantly associated with negative psychological variables, our estimated standardized effect sizes suggest that coping-motivated use might not be the only factor associated with these outcomes. Third, the sample was limited to emerging adults who had past-month MJ use, were not seeking treatment, and were sexually active. Although the sample had the strengths of not excluding those with Axis I psychopathology, being ethnically/racially diverse, being nearly half female, and including a substantial number of emerging adults not currently in school (41%), it was not an epidemiological sample. Furthermore, > 50% of potential participants declined to participate in the study. Fourth, we did not use a diagnostic measure to assess the presence of major depressive disorder. Fifth, MJ-induced anxiety is one of the most commonly reported acute symptoms of MJ use (Crippa et al., 2009). Thus, it is possible that self-reported use of MJ to cope with distress is confounded by MJ-triggered symptoms. Finally, given the cross-sectional nature of the data, we were unable to examine the temporal relationship between coping-motivated use and psychological distress. Still, our findings suggest that assessment of use of MJ to manage psychological distress may be clinically important and, if found, signal the importance of a broad and careful mental health assessment.
These results raise the question about how coping-motivated MJ use might improve or worsen one’s well-being. Although MJ may be perceived as beneficial in ameliorating symptoms of emotional distress, long-term MJ use for these purposes has been associated with deleterious consequences (Patrick, Schulenberg, O’Malley, Johnston, et al., 2011). More longitudinal research will be needed to examine if, despite being used with the intention of mitigating distress, coping-motivated use may actually worsen psychological health.
ConclusionsMJ use is becoming more socially acceptable and common in emerging adults. These results help clinicians identify MJ-using individuals who are likely to also have psychological distress symptoms. It appears that there is an important subset of emerging adults who use MJ for coping purposes, and these individuals are at risk for a variety of MJ-related problems. Clinicians working with patients who use MJ to cope with negative emotions face the challenge of confronting misconceptions about the perceived benefit of using MJ to “treat” distress. If MJ users continue to be reluctant to engage in drug counseling or to reduce use, despite having substance-related problems, clinicians must become more open to providing treatment such as alternative coping strategies for what these users might be more motivated to change, namely, their symptoms of psychological distress.
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Submitted: December 8, 2014 Revised: March 2, 2015 Accepted: March 9, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 627-632)
Accession Number: 2015-17753-001
Digital Object Identifier: 10.1037/adb0000083
Record: 16- Correlates of engaging in drug distribution in a national sample. Stanforth, Evan T.; Kostiuk, Marisa; Garriott, Patton O.; Psychology of Addictive Behaviors, Vol 30(1), Feb, 2016 pp. 138-146. Publisher: American Psychological Association; [Journal Article] Abstract: In this study, we examined self-reported behaviors and characteristics of individuals involved in drug distribution to identify correlates of engaging in drug-distribution behaviors. Correlates of interest included demographic characteristics, substance-use patterns, psychological impairment, and criminal involvement. Data from the 2012 National Survey on Drug Use and Health (U.S. Department of Health and Human Services, Substance Abuse & Mental Health Services Administration, 2013) were used for analyses (N = 55,108). A logistic regression analysis distinguished those who have sold drugs from those who have not sold drugs to identify correlates of engaging in drug distribution. Results showed that recency of substance use, severity of substance use, criminal activity, mental health diagnoses, substance-use treatment, and arrest history were all significantly associated with distribution behaviors. Findings indicate the importance of accounting for the heterogeneous characteristics of individuals involved in distribution behaviors when considering treatment options or criminal proceedings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Record: 17- Correlates of recent drug use among victimized women on probation and parole. Golder, Seana; Hall, Martin T.; Engstrom, Malitta; Higgins, George E.; Logan, TK; Psychology of Addictive Behaviors, Vol 28(4), Dec, 2014 pp. 1105-1116. Publisher: American Psychological Association; [Journal Article] Abstract: Guided by the Comprehensive Health Seeking and Coping Paradigm (CHSCP; Nyamathi, 1989), the present research sought to examine associations between victimization, psychological distress, lawbreaking and recent drug use (past 12 months) among 406 victimized women on probation and parole. Bivariate differences between women who reported recent drug use and those who did not report recent use were compared across the 4 domains of the CHSCP (sociodemographic characteristics, personal resources, lifetime victimization, dynamic crime and drug factors). Variables significantly related to recent drug use at the bivariate level were retained in the multivariate analysis. The final multivariate model, using stepwise logistic regression via backward elimination, retained five candidate variables indicating women who recently used drugs, were younger, were not sexually victimized as children, began using drugs before they were 13 years of age, were on probation, and had engaged in more recent lawbreaking. The final model accounted for approximately 30% of the variance in drug use over the past 12 months. Implications for intervention and future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Correlates of Recent Drug Use Among Victimized Women on Probation and Parole
By: Seana Golder
Kent School of Social Work, University of Louisville;
Martin T. Hall
Kent School of Social Work, University of Louisville
Malitta Engstrom
School of Social Policy and Practice, University of Pennsylvania
George E. Higgins
Department of Justice Administration, University of Louisville
TK Logan
Department of Behavioral Science and the Center on Drug and Alcohol Research, University of Kentucky
Acknowledgement: The research described here was supported, in part, by a grant from the National Institute on Drug Abuse (R01DA027981). Special thanks to all the women who have participated in this research. Additional gratitude is expressed to Robin Cook, Amy Brooks, and the Kentucky Department of Corrections, Division of Probation and Parole, for their assistance.
Drug use is a serious threat to public health and functioning among women in the criminal justice system. In fact, drug use and its associated legal penalties have fueled the growth of women in the justice system over the past 30 years. In 1980, as the War on Drugs accelerated, 13,258 women were incarcerated in state or federal prison. By 2011, that number had risen to 111,387, representing a staggering 740% increase in the number of women in prison in the United States (Carson & Sabol, 2012; The Sentencing Project, 2012). Although attention most frequently focuses on individuals who are incarcerated, the majority of justice-involved women are supervised in the community (e.g., probation, parole). Thus, as the number of women who are incarcerated has grown, so too has the number of women involved in community-based sanctions. In 1990, approximately 481,000 women were on probation in the United States. By 2011, that number had increased to nearly 1 million (Glaze, 2002; Department of Justice, 2001; Maruschak & Parks, 2012). The most recent data available indicate that 1 in every 89 women in the U.S. is under some form of correctional authority, with approximately 85% assigned to probation or parole (Pew Center on the States, 2009).
Drug-related offenses and/or drug-involvement are common among the majority of justice-involved women. Female jail and prison inmates are more likely than their male counterparts to be incarcerated for a drug offense (Carson & Sabol, 2012; James, 2004). Among women, approximately 29% of jail inmates and 25% of state prisoners have been charged with drug offenses, compared with 24% and 17%, respectively, for men in jail and state prisons (Carson & Sabol, 2012; James, 2004). In addition to the high prevalence of drug-related charges, many women in the justice system also use drugs. Data from the Arrestee Drug Abuse Monitoring Program (ADAM) found that among female arrestees, 63% tested positive for use of at least one of the following substances: cocaine, marijuana, opiates, methamphetamine or PCP (National Institute of Justice, 2003). Additionally, in a recent study of female jail detainees from across the U.S., 83% experienced a substance use disorder at some point in their lives and 53% met the diagnostic criteria for a substance use disorder in the past year (Lynch, DeHart, Belknap, & Green, 2012). In comparison, only 5.9% of females over age 12 in the general population report any substance use or dependence in the past year (5.0% for alcohol and 2.0% for other drugs; Substance Abuse and Mental Health Services Administration, 2010).
Research across numerous populations has found a consistent association between women’s substance use and psychological distress (e.g., depression, anxiety, PTSD; Chilcoat & Breslau, 1998a; Chilcoat & Breslau, 1998b; Conway, Compton, Stinson, & Grant, 2006; Cottler, Compton, Mager, Spitznagel, & Janca, 1992; Dansky et al., 1995; DeHart, Lynch, Belknap, Dass-Brailsford, & Green, 2013; Hien et al., 2010; Jané-Llopis & Matytsina, 2006; Kessler, Chiu, Demler, Merikangas, & Walters, 2005; Schiff, El-Bassel, Engstrom, & Gilbert, 2002; Watkins et al., 2004). Seminal epidemiological research documents higher prevalence of psychological distress among justice-involved women than among women in the general population (Jordan, Schlenger, Fairbank, & Caddell, 1996; Teplin, Abram, & McClelland, 1996). For example, between 13% and 17% of women in jail and prison, respectively, are found to have major depressive disorder (Jordan et al., 1996; Teplin et al., 1996). These rates are two to three times higher than rates among women in the general population. High rates of PTSD are also found among female jail detainees; 22.3% of the women in one study met the lifetime diagnostic criteria for PTSD (Teplin et al., 1996). In a more recent national survey, it was found that 43% of all female jail inmates had a serious mental illness such as depression (28%), bipolar disorder (15%), or schizophrenia (4%), whereas 53% had a lifetime diagnosis of PTSD (Lynch et al., 2012). Incarcerated women with mental health problems are three times more likely to experience addiction than those without mental health problems (James & Glaze, 2006). Other data indicate that rates of co-occurring substance use and psychological distress (e.g., PTSD, any serious mental illness) range from 39% to 46% among female jail detainees (Lynch et al., 2012). The combination of substance use and psychological distress places justice-involved women at higher risk for other negative life events: unemployment, homelessness, increased criminal justice involvement, and victimization (Baillargeon et al., 2010; Engstrom, El-Bassel, Go, & Gilbert, 2008; James & Glaze, 2006; Marquart, Brewer, Simon, & Morse, 2001).
Victimization across the life span is highly prevalent among women involved in the justice system. Although estimates vary depending on the sample, measures, and method of data collection, data indicate that between 60% and 99% of women in the criminal justice system have experienced some form of physical, sexual and/or psychological victimization in their lives (McDaniels-Wilson & Belknap, 2008; Reichert, Adams, & Bostwick, 2010). Lawbreaking and incarceration have been linked empirically and theoretically to women’s experiences of victimization (Bloom, Owen, & Covington, 2003; Browne, Miller, & Maguin, 1999; McDaniels-Wilson & Belknap, 2008; Reichert et al., 2010; Salisbury & Voorhis, 2009; Tripodi & Pettus-Davis, 2013; Widom, 1995; Widom & Ames, 1994). For example, childhood abuse and neglect increase the likelihood of arrest as a juvenile by 59%, as an adult by 28%, and for a violent crime by 30%, regardless of gender (Widom & Ames, 1994). For females in particular, those who were abused or neglected in childhood are 73% more likely than a comparison group of women to be arrested for property, alcohol, drug, violent and misdemeanor charges such as disorderly conduct, curfew violations or loitering (Widom & Ames, 1994).
An increasing body of research across diverse populations of women suggests that the association between victimization and subsequent criminal justice involvement is influenced by substance use and psychological distress (Daly, 1992–1993; DeHart et al., 2013; Salisbury & Voorhis, 2009). However, research has yet to fully examine the complex relationships between various types of victimization (e.g., childhood abuse, adult intimate partner violence (IPV), and adult nonintimate partner violence [NIPV]), psychological distress, and substance use among justice-involved women specifically. In fact, most of the research to date on justice-involved women has been descriptive in nature, highlighting prevalence rather than illuminating the relationships among these factors (for notable exceptions, see Salisbury & Voorhis, 2009; Tripodi & Pettus-Davis, 2013). Given the ways in which substance use contributes to the risk of criminal justice involvement, especially in the context of the War on Drugs, it is particularly important to understand correlates of drug use in order to design effective services that can improve well-being and reduce drug use and its multifaceted consequences for women.
In order to address this gap, the present study sought to increase understanding of the associations between different types of victimization, psychological distress, lawbreaking activity and recent drug use among women in the criminal justice system. This study was guided by an adaptation of the Comprehensive Health Seeking and Coping Paradigm (CHSCP; Nyamathi, 1989); research guided by the CHSCP has made important contributions to understanding the mechanisms associated with behaviors such as substance use and HIV risk behaviors among adults experiencing poverty and other risks (Nyamathi, Flaskerud, & Leake, 1997; Nyamathi, Keenan, & Bayley, 1998; Nyamathi et al., 1999; Nyamathi, Leake, Keenan, & Gelberg, 2000; Nyamathi et al., 2012; Nyamathi, Stein, & Bayley, 2000; Nyamathi, Stein, & Swanson, 2000). Within this framework, women’s engagement in high-risk behavior, such as recent drug use, is conceptualized as a function of a multidimensional health-seeking process that is characterized as a transaction between the individual and her environment at multiple systemic levels (Nyamathi, 1989). In the present study, sociodemographic variables and indicators of personal resources (a theoretical construct that includes measures of psychological distress, self-esteem and impulsivity), victimization, and dynamic drug and crime factors are identified as potential domains that influence women’s engagement in recent drug use. The CHSCP was used to guide the selection of relevant behavioral domains and their operationalization (e.g., personal resources) and adapted to include domains that address the unique circumstances of justice-involved women (e.g., victimization, dynamic drug and crime factors).
Although theoretically guided, this research is exploratory and not driven by a priori hypotheses about relationships among domains; rather, this research seeks to provide the necessary evidence upon which to build well-defined, population-specific models of behavior. Thus, the present study addressed the following research question: what factors (i.e., sociodemographic characteristics, personal resources, type of victimization, and dynamic drug and crime factors) are associated with recent drug use among victimized women on probation and parole? In a population where drug use and victimization are highly prevalent, findings from the present study will allow practitioners and policymakers to more easily identify and assist women at higher risk for continued substance use, and potentially, further criminal justice involvement.
Method Participants and Procedures
The sample included 406 women on probation and/or parole in Jefferson County, Kentucky. Jefferson County is a large, urban area that includes Louisville. Recruitment methods included face-to-face recruitment at all probation and parole offices located within the county; direct mailings to women on probation and parole in Jefferson County; advertisements in the local newspaper; the website Craigslist; public access TV; fliers posted in a variety of public locations (e.g., bus stops, convenience stores, apartment complexes); community-based organizations; government agencies; health care facilities; and community outreach by study personnel.
To be eligible for participation, women had to meet the following criteria: a) be on probation and parole in Jefferson County; b) be at least 18 years of age; c) report that when they had sex they either had sex with men only or with both women and men (women who had been recently incarcerated were asked about the year before incarceration); and d) report lifetime experience of physical and/or sexual victimization as a child and/or an adult from a parent/caretaker, intimate partner, and/or nonintimate partner (e.g., stranger, acquaintance). Screening for eligibility was conducted by telephone (90%) and in person (10%). Eighty-one percent of the women screened were eligible to participate. Women who were screened reported learning about the study from the following sources (participants could identify more than one source): direct mail (33%); word of mouth (e.g., a probation officer, mother, friend; 33%); fliers posted in public locations (15%); community-based organization (11%); direct contact with study personnel (9%); and newspaper/radio/Internet (2%). The most common reasons for ineligibility were not being on probation or parole, no history of victimization, and reporting only female sexual partners.
Before the interview, the women were consented using the University of Louisville Institutional Review Board approved consent form; an National Institutes of Health (NIH) Certificate of Confidentiality was obtained and documented in the consent form. All interviews were administered face-to-face by trained female staff using audio computer-assisted interviewing (ACASI; Nova Research Company, 2003) on laptop computers; on average, interviews lasted 2 and a half to 3 hrs. Participants used headphones to listen as the survey items were read to them by the computer (questions and response options were also simultaneously displayed on the computer screen); participants entered their own responses directly into the computer. Questionnaires were password-protected and response data were encrypted so that unauthorized users were unable to view, export or modify collected data without the correct password. Participants were debriefed and compensated $35 for their time.
Measures Independent Variables
Sociodemographic characteristics
Respondents’ age was provided in years. Two categories were used to describe the race/ethnicity of the participants: African American, multiracial, or other racial/ethnic background and White. Intimate partner status was assessed by three categories indicating whether a respondent reported being: single; married or cohabiting with a male sexual partner; or divorced, separated or widowed at the time of the interview. Three categories described educational attainment: less than a high school diploma/GED; high school diploma/GED; greater than a high school diploma/GED. Current employment status was dichotomous, working (1) or not working (0), and women were asked if they considered themselves homeless (yes = 1; no = 0).
Type of victimization
Victimization was operationalized by eight dichotomous variables (yes = 1; no = 0) reflecting whether a woman reported experiencing any of the following types of victimization: childhood psychological abuse, childhood physical abuse, childhood sexual abuse, psychological IPV, physical IPV, sexual IPV, physical NIPV, and sexual NIPV. This operationalization is consistent with a growing body of research that examines different types of childhood and adult victimization simultaneously (Alvarez et al., 2009; McGuigan & Middlemiss, 2005).
Variables comprised items adapted from the National Crime Victimization Survey, the Revised Conflict Tactics Scale and Tolman’s Psychological Maltreatment of Women Inventory (Straus, Hamby, Boney-McCoy, & Sugarman, 1996; Tjaden & Thoennes, 1998, 2000; Tolman, 1999; Tolman, 1989; a complete list of items is available from the first author) and have been used in prior research (Golder & Logan, 2010, 2011; Golder & Logan, 2014; Logan & Leukefeld, 2000; Logan, Walker, & Leukefeld, 2001). Childhood victimization assessed abuse by a parent and/or other caretaker when a woman was 18 or younger; IPV referred to perpetration of violence by individuals “like a boyfriend and/or husband” whereas NIPV was defined as victimization perpetrated by a stranger, acquaintance or relative (other than guardians/parents or spouses).
Psychological abuse in childhood and in the context of IPV captured a range of potentially psychologically abusive experiences (e.g., “insulted, shamed or humiliated you in front of others”; “withhold food from you as a punishment”). Physical childhood abuse assessed whether respondents had ever been physically hurt on purpose, beat up, or attacked with a weapon; physical IPV and NIPV assessed whether the corresponding person had ever: physically hurt her on purpose; caused her to have an accident; beat her up; used a knife, gun or some other thing (like a club or bat) to get something [from her]; and/or attacked her with a weapon. Childhood, IPV and NIPV sexual victimization were assessed by questions asking respondents if they had ever been forced or threatened to: do “sexual things other than sexual intercourse (e.g., petting, oral sex)”; “have sexual intercourse but it did not actually occur”; and/or “have sexual intercourse and it actually happened.”
Personal resources
Three areas were evaluated: psychological distress, self-esteem and impulsivity. Psychological distress was operationalized by two variables (general psychological distress; PTSD). General psychological distress was measured by the Global Severity Index (GSI) of the Brief Symptom Inventory (BSI; Derogatis, 1993), which yields a summary score that combines the nine symptom domains of the BSI. The individual is asked to describe her degree of distress for each psychological symptom during the past seven days; higher scores indicate higher levels of psychological distress/symptoms (range 0–4). Raw scores above .62 on the GSI indicate levels of reported psychological distress that exceed those of 84% of the national population of women and are considered to surpass the clinical threshold for presence of a mental health problem (Derogatis, 1993; Golder & Logan, 2010; Potter & Jenson, 2003). Alpha reliability for the present scale was .97.
PTSD was measured by the 49-item Posttraumatic Stress Diagnostic Scale (PDS; Foa, 1995; Foa, Cashman, Jaycox, & Perry, 1997). A single variable assessed whether or not (yes = 1; no = 0) the woman currently met the DSM–IV diagnostic criteria for PTSD. The PDS is found to be reliable, valid and have good diagnostic performance (Foa, 1995; Griffin, Uhlmansiek, Resick, & Mechanic, 2004; Powers, Gillihan, Rosenfield, Jerud, & Foa, 2012).
Self-esteem was measured by the Rosenberg Self-esteem Scale (Rosenberg, 1965). The scale has a possible range of 0 to 30, with higher scores reflecting lower self-esteem (alpha reliability in the present study = .87.). Lastly, impulsivity was measured by the Barratt Impulsiveness Scale-11, a widely used and well-validated personality measure (Patton, Stanford, & Barratt, 1995). The measure consists of 30 statements; representative items include “I don’t pay attention” and “I do things without thinking.” The response options range from 1 = “Rarely/Never” to 4 = “Almost Always/Always”; higher scores reflect higher levels of impulsivity (possible range = 30 to 120; alpha in the present study = .87).
Dynamic drug and crime involvement factors
Eight variables operationalized this construct. Previous studies have demonstrated that early substance use—even when such use does not meet criteria for a substance use disorder—is associated with an increased risk of adult illegal activity or criminality (Stenbacka & Stattin, 2007). In the current study, early drug use was operationalized as a dichotomous variable indicating whether or not a woman used any of 10 substances (i.e., marijuana, cocaine, crack, heroin, opiates other than heroin, hallucinogens, sedatives/tranquilizers/barbiturates, methamphetamine, club drugs and prescription drugs) before the age of 13 (yes = 1; no = 0); prior research has established the age of 13 as an appropriate cutpoint for early initiation of substance use (Grant & Dawson, 1998). Participants were asked whether they had ever been in alcohol or drug treatment (yes = 1; no = 0) and the total number of lifetime treatment episodes.
Correctional status was assessed by asking women to indicate whether they were on probation, parole or both. Questions adapted from the Addiction Severity Index (McLellan et al., 1992) assessed the number of days a woman reported being in a controlled environment and/or halfway house/recovery home in the past 12 months. Women reported the total number of days they had been incarcerated in the past 5 years.
Finally, two variables measured the number of lawbreaking activities a woman reported over her lifetime and over the past 12 months (possible range 0 to 8). Women were asked about their engagement in eight separate lawbreaking behaviors (e.g., “Purposely damaged or destroyed something that did not belong to you?”; “Knowingly received, bought or sold stolen goods”). Questions involving drug-involved lawbreaking were omitted to avoid overlap with the dependent variable.
Dependent Variable
Recent drug use
Because even a single episode of drug use can have significant implications for women on probation and parole, a dichotomous variable (yes = 1; no = 0) was used to assess whether a respondent reported use of any of the 10 substances identified above during the past 12 months.
Data Analysis
To address the primary research question, bivariate and multivariate analyses were conducted. Bivariate analyses were used to gain a better understanding of which independent variables best represented the theoretically specified domains under consideration. First, differences between women who reported recent drug use and those who did not report recent use were compared across the four domains of the CHSCP (sociodemographic characteristics, personal resources, lifetime victimization, dynamic crime and drug factors). Variables that evidenced a significant relationship with the dependent variable in this step were retained for inclusion in the multivariate analysis. In addition, bivariate correlations are presented for these variables, providing a measure of effect size (i.e., correlation coefficient) and a means for assessing for high multicollinearity. Finally, multivariate analysis using logistic regression was used to identify the best fitting model; tolerance statistics are presented for each variable in the model to allow for examination of multicollinearity. The overall analysis strategy is consistent with a theory-guided, model-building approach and the research question being addressed (Tabachnick & Fidell, 2001).
Results Descriptive Findings
Means or percentages, standard deviations, and range for all variables are reported in Table 1. Briefly, the women were on average 37 years of age, slightly more than half were White (50.5%), and 44.6% reported being single, not living with a male partner. Educational attainment varied; slightly more than 27% reported less than a GED or high school diploma whereas 32% reported more than a GED or high school diploma. Twenty-nine percent of the women reported working part- or full-time, and 34% considered themselves homeless. Select results for Personal Resources showed that 68.7% of the women in this sample had a level of general psychological distress that was greater than the cutoff for clinical significance and 48.5% of the women met the diagnostic criteria for PTSD.
Sample Characteristics and Between-Group Differences for Women Who Do and Do Not Report Recent Drug Use (Past 12 Months)
Approximately 70% of the women reported experiencing some form of physical and/or childhood sexual victimization, slightly more than 90% reported ever experiencing any physical and/or sexual IPV, and 72% reported physical and/or sexual NIPV sometime in their lives (data not reported in table). Rates of childhood victimization ranged from 38.7% (sexual) to 75.1% (psychological), whereas the prevalence of different types of IPV in the past 12 months ranged from 9.1% (sexual) to 49.0% (psychological). Slightly more than 7% of women reported sexual NIPV and 10.8% reported physical NIPV in the past 12 months. Approximately 42% of the women had engaged in drug use before the age of 13. Two-thirds of the participants had been in drug treatment sometime in their lives. The majority of women were on probation (75.6%); they had spent approximately 47 days in a controlled environment in the past 12 months and 286 days incarcerated in the past 5 years. On average, the women had engaged in almost four (3.78) different lawbreaking behaviors over their lives and about two (1.84) lawbreaking behaviors in the past year.
Approximately 46% of the women reported recent drug use. Regarding the types of substances that were used in the past 12 months, marijuana was the most common (27.9%) followed by opiates other than heroin (19.8%), cocaine (18.0%), sedatives/tranquilizers/barbiturates (17.0%), crack (14.8%), prescription drugs (13.1%), heroin (6.2%), club drugs (2.7%) and hallucinogens (.7%; data not reported in table).
Bivariate Analysis
Significant associations were found between 14 of the independent variables and the dependent variable. Women who used drugs were more likely to be younger than women who did not use drugs in the past 12 months and to report higher levels of general psychological distress (1.29 compared with 1.08) and PTSD (54.5% compared with 43.4%). With the exception of childhood psychological and physical victimization, there were statistically significant differences on all the measures of victimization between women who reported recent drug use and those who did not report recent drug use. Generally, victimization was associated with drug use; however, women who reported experiencing childhood sexual victimization were less likely to report recent drug use (33.2% compared with 43.4%). Regarding the dynamic drug and crime factors, women who used drugs recently were more likely to report initiating drug use at an early age (52.9% compared with 32.4%) and to have been in drug treatment in the past 12 months (46.0% compared with 35.2%). However, there were no significant differences in the number of times each group of women reported being in drug treatment. Finally, women who used drugs recently were more likely to be on probation (88.2%), to have spent fewer days incarcerated in the past 5 years (an average of 237.10 days in the past 5 years as compared with 327.94 days), and to have engaged in more lawbreaking behaviors across the two measured periods (4.18 and 2.42 ever and in the past 12 months, respectively). The correlation matrix is found in Table 2; there is no evidence of high multicollinearity among the variables (Allison, 2012).
Bivariate Correlation Matrix
Multivariate Analysis
The test of the final model, against a constant-only model was statistically reliable, chi-square (14, N = 399) = 102.001, p ≤ .01, indicating that the predictors, as a set, reliably distinguished between women who used drugs in the past 12 months and those who did not (−2LL = 446.237). The final model accounted for 30% (Nagelkerke R2: .303) of the variance in drug use over the past 12 months (Tabachnick & Fidell, 2001). The overall prediction success for this model was 71.8%, which was greater than the proportional by chance accuracy rate of 62.75%, thus supporting the utility of the model. More specifically, 77.5% of the women who reported not using drugs in the past 12 months and 65.2% of those who did use drugs were correctly predicted by the model.
Table 3 shows the regression coefficients, standard errors (SE), odds ratios (OR), 95% confidence intervals (CI), and tolerance for the final model. Five variables in the model reached the conventional level of significance: age, childhood sexual victimization, early drug use, correctional status, and lawbreaking (past 12 months). There was an inverse relationship (OR = .96; 95% CIs [.94, .99] between age and the dependent variable: older age was associated with slightly reduced odds of drug use in the past year. Similarly, there was an inverse relationship between childhood sexual victimization and recent drug use such that women who were sexually victimized as children were considerably less likely to report recent drug use than women who had not experienced this type of victimization (OR = .56; 95% CI = [.34, .91] Early drug use (OR = 2.63; 95% CI [1.60, 4.32] and engaging in more lawbreaking behaviors during the past 12 months (OR = 1.75; 95% CI [1.32, 2.31] were associated with recent drug use. Finally, correctional status was inversely related to the dependent variable (OR = .26; 95% CI [.13, .50] the odds of women on parole reporting drug use in the past 12 months were less than those of women on probation. Confirming the interpretation of the bivariate correlations, tolerance values were all well above .10; larger values (i.e., those closer to 1.0) are indicative of fewer problems with multicollinearity (Denis, 2011; Regression with SPSS Chapter 2: Regression Diagnostics).
Logistic Regression Model Predicting Recent Substance Use (Past 12 Months)
DiscussionDrug use plays a major role in women’s involvement in the criminal justice system. Identifying and understanding factors associated with this high-risk behavior is essential in order to address it. This is the first known study to elucidate the associations between types of victimization, psychological distress and recent drug use among women on probation and parole. When controlling for confounding factors, the multivariate findings indicate that women who recently used drugs were younger, were not sexually victimized as children, began using drugs before they were 13 years of age, were on probation and had engaged in more recent lawbreaking. In terms of the CHSCP framework informing the study, these findings suggest that domains related to sociodemographic characteristics, victimization, drug use, illegal activity and correctional system status are salient correlates in recent drug use among women on probation and parole involved in this study. In order to further consider the relationships identified in the multivariate analysis, each of the significant predictors is considered in turn.
The association between age and substance use in the general population is well established. Research has identified an age-related pattern of drug and alcohol use that begins in adolescence, peaks in young adulthood, and then declines (Bachman, Wadsworth, O’Malley, Johnston, & Schulenberg, 1997; Chassin, Fora, & King, 2004; Chen & Kandel, 1995; van Lier, Vitaro, Barker, Koot, & Tremblay, 2009; Roettger, Swisher, Kuhl, & Chavez, 2011; Tobler & Komro, 2010). This pattern is evidenced in the most recent National Survey on Drug Use and Health: rates of substance dependence or abuse were higher among younger adults (age 18 to 25; 18.6%) than among youth (age 12 to 17; 6.9%) or adults 26 and older (age 26 and older; 6.3%; Substance Abuse and Mental Health Services Administration, 2012). Notwithstanding the general relationship between age and substance use, methodological advances have highlighted the heterogeneity found among people who use drugs and have contributed to a more nuanced understanding of different patterns of substance use across the life course (Guo et al., 2002; Hser, Anglin, Grella, Longshore, & Prendergast, 1997; Hser, Huang, Brecht, Li, & Evans, 2008; Kertesz et al., 2012). Research focused on adults with substance use histories suggests that the relationship between age and drug use behaviors is complex and does not necessarily decline with age (for discussion see Engstrom, Shibusawa, El-Bassel, & Gilbert, 2011). In fact, research with 60 incarcerated women in Kentucky found similar rates of recent substance use among older and younger women (Stanton, Walker, & Leukefeld, 2003).
Relatedly, there is a growing body of research that has identified a variety of distinct subgroups and trajectories among people who use drugs, with certain attention given to age at initiation (Chassin et al., 2004). In particular, early onset of drug use (as well as illegal activity) is associated with more severe and persistent drug use patterns (Hser et al., 2008; Hser, Longshore, & Anglin, 2007). Consistent with these findings, the present study found that odds of recent drug use were 2.45 times as large for women who used drugs before the age of 13 than for women who did not experience early drug use. The aforementioned results, together with research indicating that people with younger age of drug initiation and more involvement in lawbreaking may be less responsive to treatment (Grella & Lovinger, 2011), suggest that victimized women on probation and parole who initiated early drug use may be at particularly high risk for ongoing substance use and may require innovative outreach and intervention strategies. One approach that may hold promise for adaptation is the Comprehensive Gang Model. This model, which has been tested and refined in more than 20 communities in the U.S. since the 1990s, employs a comprehensive set of strategies that address both structural/community-level issues and individual-level approaches to address a wide range of needs (i.e., psychological distress, educational attainment) among gang-involved youth (Office of Juvenile Justice and Delinquency Prevention, 2010; Spergel, 1995). The specific strategies include: community mobilization involving citizens, former gang members (in the case of adaptation, these strategies would focus on justice-involved women rather than gang members), community groups and agencies in the coordination of community-based programming; opportunity for justice-involved youth to engage in education, training and employment programs; social intervention focused on linking justice-involved youth and their families to needed services; use of close supervision and monitoring by the criminal justice system, as well as community-based organization; and development and implementation of policies and procedures that capitalize on available and existing resources. Unfortunately, evidence-supported prevention programs such as the Comprehensive Gang Model have not been widely adopted in the U.S.
The relationship between child sexual abuse and later substance use among women is well documented (Gutierres & Puymbroeck, 2006; Ireland & Widom, 1994; Widom, Marmorstein, & White, 2006). As such, it is not unexpected that rates of child sexual victimization and recent drug use among this sample appear high. For example, the prevalence of child sexual abuse among a national sample of older adolescents indicates that among females 17 and younger, 26.6% and 5.5% report an incident of sexual abuse/assault by any type of perpetrator or family member, respectively (Finkelhor, Shattuck, Turner, & Hamby, 2014). In comparison, 38.7% of the women in the present sample reported experiencing childhood sexual victimization perpetrated by a parent and/or caretaker. Similarly, among all women 12 and over, only 12.5% report any past year use of illicit substances compared with 46% of the women in this study (Substance Abuse & Mental Health Services, 2009, 2010). Although the current study found high prevalence of childhood sexual victimization and recent drug use among participants, it also found an inverse relationship between childhood sexual victimization and recent drug use. Among women in this study, experiencing childhood sexual abuse was associated with a 44% reduction in odds of recent drug use. Thus, the relationship between these factors among this sample of highly victimized women appears complicated and may reflect the particular roles of current victimization, psychological distress and other factors in recent drug use. Future research may examine differences between the women who have been victimized sexually as children and those who have not in order to better understand these relationships. More specifically, research should determine if there are groups of women who have distinct profiles based on indicators of lifetime violence and how those profiles are associated with drug use and other indicators of psychosocial functioning.
Finally, two dynamic drug and crime factors were also significant predictors of recent drug use: correctional status and engaging in more recent lawbreaking behavior. The odds were greater that probationers, as compared with parolees, would report recent drug use (OR = .26; 95% CI [.14, .46] It may be that this finding indicates the presence of different trajectories of drug use (and/or lawbreaking behaviors) captured by the categorization/assignment of women to probation and parole. In fact, probation and parole represent different points on the criminal justice continuum (Center for Substance Abuse Treatment, 2005). As such, entry into, conditions of, and consequences for failure to comply with conditions may differ by type of supervision, thus influencing women’s engagement in drug-using behavior. For example, a common condition of both probation and parole is the prohibition of drug use. For parolees, who have experienced a period of incarceration, the risk of revocation and subsequent reincarceration that is associated with drug use may act as an inhibiting factor. In contrast, women on probation may or may not have experienced a sustained period of incarceration. Thus the possibility of incarceration may be less threatening and may exert less influence on their decision to use drugs. Future longitudinal research will provide the opportunity to examine this supposition and provide data on associations between correctional status/involvement and long-term patterns of substance use among this population.
Similarly, the association between recent lawbreaking and recent drug use appears to reflect the complicated, multidimensional relationship between lawbreaking and drug use. Specifically, the relationship between drug use and lawbreaking has been characterized as reciprocal in nature, such that there is a “multiplier” effect through which more engagement in one is associated with more engagement in the other (Prendergast, Huang, & Hser, 2008). Again, longitudinal research that provides an opportunity to track trajectories of both substance use and lawbreaking is needed to further clarify these complex relationships. Although there remains a need for future investigations, decades of research have consistently found that “substance abuse treatment offers the best strategy for interrupting the drug abuse/criminal justice cycle” (National Institute on Drug Abuse, 2012, p. 13), particularly in the context of current drug-related legislation and criminal penalties in the U.S. This large body of research, together with the findings from the current study, underscore the critical need to identify effective strategies to engage more justice-involved women in substance use treatment that is both trauma-informed and guided by principles of effective treatment for people involved in illegal activity and the criminal justice system (Marlowe, 2003; Miller & Najavits, 2012).
These findings should be considered in light of the limitations. Although it is estimated that the sample comprised approximately one-fifth of all women on probation and parole in Jefferson County at the time recruitment was initiated (Kentucky Department of Corrections, Division of Probation and Parole, personal communication, November 5, 2010), the generalizability is limited by nonrandom sampling. Only victimized women were included in this sample; thus, comparisons between nonvictimized and victimized women were not possible. However, given the prevalence of victimization among justice-involved women, the most relevant questions are not limited to differences between victimized and nonvictimized women. Rather, research that helps elucidate the heterogeneous nature of victimization (i.e., type of victimization as measured in the current study, nature of relationships involving violence, severity of violence and so on . . .) and the subsequent effects on behavior are also necessary to inform development of targeted interventions for this population. Notwithstanding, these results are not generalizable to all women on probation and parole. Women who reported only having sex with other women were excluded from participation. Intimate partner violence between same-gender female partners is an important and understudied issue. The dynamics of intimate partner violence between same-gender partners may be similar to and/or distinct from violence between opposite-gender partners; however, this empirical question/issue was outside the focus of the present study. Additionally, there was concern that inclusion of women who only had sex with other women would yield a subsample that was too small for meaningful analysis; examination of screening data indicated that only 4% of all women screened were excluded because they reported only same-gender sexual behavior. Further attention to this group of justice-involved women is an important priority for future research. Dichotomizing race/ethnicity does not allow for in-depth assessments of potential race- and ethnicity-related differences in recent drug use; future research would benefit from sampling and recruitment strategies that support adequate statistical power to conduct meaningful analysis in this area. Similarly, although the detection of any drug use is reason for violation and potential incarceration for individuals on probation and parole, measuring recent drug use without consideration of the quantity/frequency of use may not capture severity, or allow consideration of other indices of problematic use, including patterns and consequences of use. Further, the cross-sectional design limits the ability to determine temporal ordering of the variables; as such, inferences regarding directionality of associations with drug use should be viewed cautiously. This study relied on self-reports of sensitive information that may have yielded underreports of some behaviors; however, this risk was minimized with the use of ACASI technology, which enhances reporting of potentially sensitive information (Wolff & Shi, 2012).
Despite its limitations, this study makes important contributions to our understanding of recent drug use among women on probation and parole. The CHSCP offers a broad theoretical framework from which to examine complex relationships between sociodemographic characteristics, victimization, psychological distress, lawbreaking activity, justice system involvement, and drug use among this population. As such, it assists with informing multidimensional intervention strategies that may provide key avenues to reduced drug use among this population of women. Most notably, the findings of the present study highlight the potential value of services that can effectively address risks associated with younger age, early initiation of drug use, probation status, and ongoing involvement in lawbreaking activity among women on probation and parole with histories of victimization.
Footnotes 1 The 2000 ADAM report marks the last time data were available for female arrestees. In the case of female and male arrestees, the number of men and women testing positive for these substances at arrest were almost identical, with 64% of men testing positive.
2 A total of 7.6% of the respondents reported being Latina, Native American, Asian, multiracial or other racial/ethnic identity; the majority of these individuals (3.2%) identified as multiracial. Because the individual categories were too small for meaningful analysis, the groups were collapsed into a single group.
3 Prescription drug misuse was operationalized as ever using “prescription drugs that were not prescribed to you, in excess of what was prescribed for you, and/or for recreational purposes.”
4 The proportional by chance accuracy rate was computed by calculating the proportion of cases for each group (i.e., those cases that are correctly predicted and those that are incorrectly predicted) in the classification table at Step 0 and then squaring and summing the proportion of those cases for each group (0.4632 + 0.5372 = 0.502). Based on these calculations, the proportional by chance accuracy criteria is 62.75% (1.25 × 50.2% = 62.75%; Bayaga, 2010).
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Submitted: August 20, 2013 Revised: August 7, 2014 Accepted: August 19, 2014
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Source: Psychology of Addictive Behaviors. Vol. 28. (4), Dec, 2014 pp. 1105-1116)
Accession Number: 2014-56246-004
Digital Object Identifier: 10.1037/a0038351
Record: 18- Daily associations between PTSD, drinking, and self-appraised alcohol-related problems. Wilson, Sarah M.; Krenek, Marketa; Dennis, Paul A.; Yard, Samantha S.; Browne, Kendall C.; Simpson, Tracy L.; Psychology of Addictive Behaviors, Vol 31(1), Feb, 2017 pp. 27-35. Publisher: American Psychological Association; [Journal Article] Abstract: Alcohol dependence (AD) and posttraumatic stress disorder (PTSD) are highly comorbid, yet limited research has focused on PTSD and daily drinking as they relate to self-appraised alcohol-related problems. In treatment contexts, patients’ appraisals of alcohol-related problems have implications for assessment, intervention strategies, and prognosis. This study investigated the moderating effect of within-person (daily symptoms) and between-person (overall severity) differences in PTSD on the association between daily drinking and same-day alcohol-related problems. Participants with comorbid AD and PTSD (N = 86) completed 1 week of Interactive Voice Recognition data collection, and logistic and γ-adjusted multilevel models were used to estimate odds and magnitude of self-appraised alcohol-related problems. Results revealed that both within-person and between-person PTSD moderated the association between number of drinks and severity of self-appraised problems. As within-person and between-person PTSD symptoms increased, there was a weaker association between number of drinks consumed and perceived alcohol-related problems. Contrasts further revealed that on nondrinking and light-drinking days, PTSD (both daily symptoms and overall severity) was positively associated with ratings of alcohol-related problems. However, PTSD was not associated with alcohol-related problems on heavier drinking days. In conclusion, more severe PTSD is associated with a less directly contingent relationship between drinking quantity and perceived alcohol-related problems. These findings suggest the importance of further investigations of this moderating effect as well as clinical treatment of comorbid AD and severe PTSD with functional analysis of drinking. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Daily Associations Between PTSD, Drinking, and Self-Appraised Alcohol-Related Problems
By: Sarah M. Wilson
Mid-Atlantic Mental Illness Research Education and Clinical Center (MIRECC), Durham, North Carolina and Durham VA Health Care System, Durham, North Carolina;
Marketa Krenek
VISN 20 MIRECC, VA Puget Sound Health Care System, Seattle, Washington
Paul A. Dennis
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center and Durham VA Health Care System, Durham, North Carolina
Samantha S. Yard
VA Puget Sound Health Care System
Kendall C. Browne
VISN 20 MIRECC, VA Puget Sound Health Care System and Department of Psychiatry & Behavioral Sciences, University of Washington
Tracy L. Simpson
Center of Excellence in Substance Abuse Treatment & Education (CESATE), VA Puget Sound Health Care System and Department of Psychiatry & Behavioral Sciences, University of Washington
Acknowledgement: This study was supported in part by a grant from NIH/NIAAA award (R21AA17130-01; PI: TLS) and by resources from the Center of Excellence in Substance Abuse Treatment and Education (CESATE; TLS), the VISN 20 and VISN 6 Mental Illness Research, Education, and Clinical Centers (MIRECC), the U.S. Department of Veterans Affairs Office of Academic Affiliations, Advanced Fellowship Program in Mental Illness Research and Treatment (SMW, MK, SSY, and KCB), and the VA Puget Sound Health Care System, Seattle, WA. The views expressed in this article are those of the authors and do not represent the views of the Department of Veteran Affairs or the United States government. Findings from the current study were disseminated previously as a poster presentation at the meeting of the International Society for Traumatic Stress Studies (Wilson, Krenek, Browne, Yard, & Simpson, 2015). The data used in the current study have yielded previously published articles (Browne, Wray, Stappenbeck, Krenek, & Simpson, 2016; Krenek, Lyons, & Simpson, 2016; Lehavot, Stappenbeck, Luterek, Kaysen, & Simpson, 2014; Stappenbeck, et al., 2015; Simpson, Stappenbeck, Luterek, Lehavot, & Kaysen, 2014).
Alcohol use disorder (AUD) and posttraumatic stress disorder (PTSD) often co-occur. Among individuals with PTSD, AUD is quite common, with estimates of lifetime prevalence ranging from 22–75% and with population-level estimates at 42% (Grant et al., 2015; Jacobsen, Southwick, & Kosten, 2001; Pietrzak, Goldstein, Southwick, & Grant, 2011). Similarly, individuals with lifetime AUD are at increased risk for lifetime PTSD (Goldstein et al., 2016). Much of the impairment associated with comorbid PTSD and AUD manifests with regard to difficulties in relationships, occupational impairment, legal problems, and health concerns (Blanco et al., 2013; Drapkin et al., 2011). For example, both PTSD and AUD increase the risk for health-related problems and all-cause mortality (Boscarino & Figley, 2009; Chwastiak, Rosenheck, Desai, & Kazis, 2010; Pacella, Hruska, & Delahanty, 2013), and their comorbidity is associated with greater physical health concerns and lower quality of life than seen for those with one or the other disorder (Evren et al., 2011; Kaysen et al., 2008). Although PTSD and AUD often exacerbate each other (Marshall et al., 2012; Oslin, Cary, Slaymaker, Colleran, & Blow, 2009; Wolitzky-Taylor, Bobova, Zinbarg, Mineka, & Craske, 2012), limited research has addressed the interplay between PTSD symptoms, drinking, and alcohol-related problems.
There is evidence that PTSD symptomatology is associated with AUD severity in treatment-seeking populations, and moreover this effect is not explained by quantity of alcohol consumed. In a sample of treatment-seeking U.S. veterans, those with comorbid PTSD and AUD were compared with individuals with AUD alone (Fuehrlein, Ralevski, O’Brien, Jane, Arias, & Petrakis, 2014). Although they had significantly fewer drinks per day and fewer heavy drinking days, participants with comorbid AUD/PTSD presented with significantly higher alcohol dependence scores (Fuehrlein et al., 2014). As this finding suggests, those with comorbid PTSD have AUD symptoms that may not be completely contingent upon quantity of alcohol consumed. In another study of treatment-seeking veterans, despite having fewer years drinking and no difference in current drinking quantity/frequency, those with comorbid PTSD/AUD reported greater alcohol dependence (AD; Petrakis et al., 2006). Taken together, these findings suggest that among individuals with AUD, comorbid PTSD may exacerbate alcohol’s impact on alcohol-related problems.
Building upon cross-sectional evidence of elevated alcohol problems in those with comorbid PTSD/AUD, Gaher and colleagues (2014) used experience sampling to examine within-person and between-person relationships between PTSD, drinking, and self-rated drinking problems. To examine daily and overall relationships with alcohol-related problems, it was necessary to statistically disaggregate effects of within-person PTSD and drinking (daily levels) from between-person PTSD and drinking (overall levels). In the study, within-person PTSD and drinking were assessed multiple times per day, and between-person PTSD was assessed by averaging each participant’s responses over the 2 weeks of monitoring. The authors showed that after controlling for the effects of drinking, both within-person and between-person PTSD independently affect self-ratings of alcohol-related problems. Within-person daily PTSD symptoms were associated with increased alcohol-related problems reported later the same day after controlling for quantity of alcohol consumed. Similarly, those with more severe between-person PTSD tended to report more alcohol-related problems after controlling for alcohol consumption.
Despite evidence that PTSD affects alcohol-related problems after controlling for drinking quantity, it remains unknown whether PTSD moderates the relationship between drinking amount and perceived alcohol-related problems. If PTSD severity does have a moderating effect on the relationship between drinking quantity and self-ratings of alcohol-related problems in treatment-seeking populations with comorbid PTSD/AUD, this could potentially affect PTSD/AUD treatment strategies. According to prevailing theoretical orientations to AUD treatment, behavior change is made possible in part through awareness and accurate assessment of alcohol-related problems (Donovan, 2003; Prochaska & Velicer, 1997). For individuals entering alcohol treatment, the more accurate they are in their self-appraisal of their pretreatment alcohol-related problems, the more positive their treatment outcome (Sawayama et al., 2012). Given that those with unremitting PTSD fare worse in AUD treatment outcome (Read, Brown, & Kahler, 2004), it is possible that PTSD contributes to this disparity by either exacerbating alcohol-related problems or disrupting accurate self-rating of alcohol-related problems.
The Present StudyWhile the aforementioned evidence clarifies that PTSD symptomatology adds unique variance to daily self-ratings of alcohol-related problems, we currently do not know whether PTSD symptoms moderate the cross-sectional association between drinking behavior and self-reports of alcohol-related problems. Varying levels of daily and overall PTSD symptom severity may differentially interact with heavy and lighter alcohol consumption to inform alcohol-related problem ratings. Both within person daily levels of PTSD and between person overall levels of PTSD could interact with daily drinking amounts. Thus, the present study used pretreatment daily monitoring data from an experimental treatment study (Simpson et al., 2014) to test the hypothesis that the association between daily drinking and same-day alcohol-related problems varies as a function of within-person PTSD (same-day symptoms) and between-person PTSD (overall severity).
Method Participants
Study participants were adults with concurrent diagnoses of AD and PTSD who indicated a desire to decrease alcohol use as part of a larger brief intervention study registered through ClinicalTrials.gov (Protocol #: NCT00760994). Briefly, study inclusion criteria were the following: (a) at least 18 years of age, (b) current AD diagnosis as defined by the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM–IV–TR; American Psychiatric Association, 2000), (c) alcohol use within the past 2 weeks, (d) current DSM–IV PTSD diagnosis, (e) capacity to provide informed consent, and (f) telephone access. Exclusion criteria were as follows: (a) history of delirium tremens or seizures, (b) opiate use or chronic treatment with opioid-containing medications during the past month, (c) Antabuse or naltrexone treatment, (d) alcohol withdrawal symptoms at initial consent, (e) acute suicidality/homicidality with intent/plan, or (f) psychosis. See Simpson et al. (2014), for further information on study participants and full inclusion/exclusion criteria. Data were excluded from the present analysis for participants with less than 50% adherence to daily monitoring to minimize nonresponse bias.
Ninety-two participants met the study entry criteria, and the final sample consisted of those who had at least 50% adherence on the daily monitoring protocol. Three participants were excluded because they did not complete monitoring days, and an additional three were excluded because they had less than 50% adherence on daily monitoring, yielding a final sample size of 86. Participants were on average 44.7 years of age (SD = 11.0), and ages ranged from 21 to 63. Ethnic group breakdown was as follows: 43.0% African American, 39.5% European American, 4.7% Hispanic/Latino, 4.7% American Indian, 1.2% Asian American, and 7.0% other ethnicity or missing. Almost half of the study sample was women (49.0%) and just over a quarter were veterans (25.6%). Only 13.1% were currently married, nearly a quarter were homeless (23.3%), and 12.8% were employed at least part-time, 10.5% were students, 45.3% were retired or disabled, and 31.4% were unemployed.
Procedure
Participants were recruited through newspaper advertisements and flyers. Participants completed an initial phone screen and then came into the lab where they provided written informed consent, underwent further screening for study inclusion, and a baseline assessment consisting of interview and self-report measures. They were compensated $30 for completing the baseline assessment. Participants received instruction on the telephone daily Interactive Voice Response (IVR) protocol. All study procedures were approved by the VA Puget Sound Health Care System Human Subjects Division Internal Review Board.
Participants completed self-report measures daily by calling a designated telephone number and answering prompts with IVR. Compliance was tracked automatically, and participants who did not call were personally contacted within two business days to collect data (10% of calls were collected verbally). Compensation was $1 for every completed day of monitoring, with a $10 bonus for seven consecutive monitoring days or a $7 bonus for six consecutive days. The time interval of IVR data collection was targeted for the 7 days before receipt of a brief intervention but this pretreatment baseline period ranged from 6 to 20 days because of scheduling difficulties. For additional information regarding procedures, see Simpson et al. (2014).
For the 86 participants who completed IVR monitoring, available observations ranged from 4 to 16 days (M = 7.3, SD = 2.6), with 659 total possible observations for all participants. The final IVR dataset, including all entries with available outcome data, contained 620 daily observations.
Measures
Screening assessment
Inclusion and exclusion criteria were assessed with the following measures: Hamilton Depression Inventory to assess suicidality (Hamilton, 1960); Structured Clinical Interview for DSM–IV Axis I Disorders to assess alcohol dependence, opiate use, and psychotic disorder (First, Gibbon, Spitzer, & Williams, 1995); PTSD Symptom Scale-Interview Version (PSS-I) to ascertain past-month PTSD diagnosis (Foa, Riggs, Dancu, & Rothbaum, 1993); and Form-42 (adapted from Form-90; Miller & Del Boca, 1994) to ensure alcohol use within the past 2 weeks. For additional information regarding these measures, see Simpson et al., 2014.
Baseline demographics
Participants were asked to identify age, gender, and veteran status as demographic covariates, and gender and veteran status were coded dichotomously (veteran status, 0 = nonveteran, 1 = veteran; gender, 0 = male, 1 = female).
Baseline PTSD
Participants were rated on baseline PTSD symptoms using the PSS-I, which is a 20- to 30-min, 17-item semistructured interview assessing DSM–IV symptoms of PTSD. The interviewer rates the severity of each symptom on a scale ranging from 0 (not at all) to 3 (5 or more times per week/very much), which yields a total score ranging from 0 to 51. The PSS-I shows excellent validity when compared to the Clinician-Administered PTSD Scale (Foa & Tolin, 2000). The PSS-I had good internal reliability in the current sample (α = .81).
Daily IVR self-report
Participants self-reported alcohol intake, PTSD symptoms, and alcohol-related problems daily using IVR telephone monitoring. Day of week (weekday = 0, weekend day = 1) was also recorded. Use of daily self-report IVR minimizes recall bias and underreporting of drinks per day (Searles, Helzer, Rose, & Badger, 2002).
IVR drinking
Participants were queried regarding the number of standard drinks consumed the day prior (beer, wine, and liquor, respectively). The number of each type of standard drink consumed each day was summed to yield a total drinks per day variable. Abnormally high values on this measure were verified verbally with participants. This methodology has been previously validated against retrospective self-report (Krenek, Lyons, & Simpson, 2016).
IVR PTSD symptoms
As described in Simpson et al., 2014, items assessing daily PTSD symptomatology were adapted from the PCL-C (King, Leskin, King, & Weathers, 1998). Items included three re-experiencing symptoms (intrusive thoughts, nightmares, and upset because of reminders), two avoidance symptoms (avoidance of thoughts and feelings associated with event, avoidance of trauma reminders), three emotional numbing symptoms (loss of interest, feeling detached, and emotionally numb), and four hyperarousal symptoms (concentration problems, alertness/vigilance, exaggerated startle, and anger/irritability). Items were chosen based on those that load most strongly in factor analytic studies of PTSD (e.g., King et al., 1998; Krause, Kaltman, Goodman, & Dutton, 2007; Palmieri, Weathers, Difede, & King, 2007) and that were likely to vary daily. Participants indicated how bothered they were on the previous day by each symptom on 9-point scales ranging from 0 = not at all to 8 = all the time. Items were averaged to create a daily IVR PTSD severity score (α = .94).
IVR alcohol-related problems
Self-appraised alcohol-related problems were assessed with a single item, “Yesterday, to what extent did you experience any negative consequences or problems related to your drinking?” Participants indicated a response on a 9-point scale ranging from 0 = none at all to 8 = worse ever. This single-item method of assessing daily alcohol-related problems was adapted from Searles and colleagues (2000).
Data Analytic Approach
Missingness was assessed for any associations with key variables at the between-subjects level (gender, veteran status, ethnicity, and baseline PTSD severity), and the within-subject level (weekend/weekday). Given nesting within the IVR data (daily self-reports nested within person), multilevel modeling (MLM) was used to analyze the data. The continuous outcome variable, daily alcohol-related problems, was zero-inflated (48.2% of responses were 0) and overdispersed (M = 2.16, SD = 2.53), corresponding to a zero-inflated gamma distribution. As such, we modeled daily alcohol-related problems via a two-part analysis. First, we used logistic MLM to model the odds of reporting any alcohol-related problems on a given day as a function of whether or not alcoholic drinks were consumed that day and PTSD symptom severity. Then, we used gamma-adjusted MLM to model nonzero alcohol problem ratings as a function of number of alcoholic drinks consumed and PTSD symptom severity.
Given that PTSD symptom severity data were collected longitudinally over a period of several monitoring days, it was possible to disaggregate within-person and between-person effects of PTSD symptom severity on alcohol problems (Curran & Bauer, 2011). To do so, we mean-standardized (i.e., z-scored) PTSD severity to statistically partial out effects of within-person daily PTSD symptoms opposed to overall, between-person PTSD symptoms over the entire monitoring period. Within-person PTSD severity was person-mean standardized (PMS) to capture the extent to which PTSD symptoms deviated from each participant’s personal mean on each day of monitoring. In other words, PMS PTSD reflects how mild/severe the participants’ PTSD symptoms were each day compared with their own personal average. We calculated between-person PTSD by grand-mean standardizing (GMS) each person’s overall PTSD scores. GMS PTSD, therefore, quantified the relative severity of each participant’s PTSD over the entire IVR monitoring period compared with others in the sample.
In each of the two models, we modeled the main effects of drinking and PTSD symptom severity on alcohol problems, and their interactions. In the logistic analysis, we examined the cross-level interaction between GMS PTSD (between-person differences in PTSD severity) and number of drinks consumed that day (Number of Drinks × Overall PTSD). We also examined the within-level interaction between PMS PTSD (within-person differences in PTSD) and number of drinks consumed that day (Number of Drinks × Daily PTSD). In the gamma model, we examined the interaction between GMS PTSD and number of drinks consumed that day (Number of Drinks × Overall PTSD) as well as the interaction between PMS PTSD and number of drinks consumed that day (Number of Drinks × Daily PTSD). In addition to these variables, veteran status, gender, age, time (days since beginning IVR monitoring), and weekend day versus weekday were covaried in both models. Logistic and gamma-adjusted MLM were conducted using PROC GLIMMIX, available in SAS (Version 9.4).
ResultsPrior preliminary analyses did not yield any significant differences between those included (n = 86) and excluded (n = 6) from the analyses with regards to baseline demographic characteristics or baseline PTSD symptomatology (see Simpson et al., 2014). Regarding missing IVR observations, there were no statistically significant patterns of missingness with regard to gender, veteran status, ethnicity, baseline PTSD severity, or weekend/weekday. At baseline, the average PSS-I score was 29.5 (SD = 9.2). See Table 1 for within-person and between-person variable mean values and SDs for the entire monitoring period. As a preliminary step, we explored within-person and between-person variability in IVR PTSD symptoms over the monitoring period by calculating an intraclass correlation (ICC) for the normally distributed IVR PTSD variable (Hoffman & Stawski, 2009).The ICC (0.695) indicated that 69.5% of the variability in PTSD symptoms was at the between-person level and 30.5% of the variability was at the within-person level.
Between-Subject and Within-Subject Descriptive Statistics, N = 620 Observations Among N = 86 Participants
Results from the logistic and gamma multilevel models are displayed in Table 2. According to the logistic model, the odds of reporting any alcohol-related problems increased when any alcohol was consumed, and individuals with higher between-person GMS PTSD severity were more likely to report having had an alcohol-related problem. Neither of the interactions between PTSD symptom level (PMS and GMS) and number of drinks consumed were significant.
Multilevel Models of Daily Alcohol-Related Problems
Turning to the gamma model, the main effects of number of drinks consumed, daily PMS PTSD symptom scores, and between-person GMS PTSD severity were each positively associated with number of alcohol-related problems reported. These main effects were qualified by significant interactions. According to the Number of Drinks × Daily PTSD interaction, the effect of number of drinks on alcohol-related problems varied as a function of daily PTSD level. At low levels of daily PTSD (1 SD below the mean) the multiplicative effect of each additional drink on self-reported problems was 2.03 (p < .001), whereas it was 1.87 (p < .001) at high levels of daily PTSD (1 SD above the mean). Contrasts in number of alcohol-related problems reported by daily PTSD level were most evident when few drinks were consumed. On days when more drinks consumed, there was less contrast in alcohol-related problems reported by daily PTSD level. There was a small effect of within-person daily PTSD on ratings of alcohol-related problems on nondrinking days (Cohen’s d = 0.25, p = .024) and on light drinking days (defined as three drinks consumed; Cohen’s d = 0.24, p = .045). There was no significant effect of within-person PTSD on self-rated problems for heavy drinking days (defined as nine drinks consumed; Cohen’s d = 0.05, p = .68).
The moderating effect of PTSD was even more pronounced in the Number of Drinks × Overall PTSD interaction (see Figure 1). According to that, the effect of number of drinks on alcohol-related problems varied as a function of overall PTSD severity level. As seen in Figure 1, at low levels of between-person PTSD (1 SD below the mean), the multiplicative effect of each additional drink on self-reported problems was 2.29 (p < .001), whereas it was 1.65 (p < .001) at high levels of PTSD (1 SD above the mean). Thus, the association between number of drinks consumed and self-rated alcohol-related problems was weaker for those with more severe between-person PTSD. The greatest contrasts in reported alcohol-related problems by individual differences in PTSD symptom level occurred on nondrinking days (Cohen’s d = 0.45, p < .001) and light-drinking days (Cohen’s d = 0.30, p = .007). However, as the number of drinks increased, there was less of an effect of between-person differences in PTSD symptom level on number of alcohol-related problems reported. At high levels of drinking (i.e., nine drinks), there was a trend toward a small inverse association between PTSD severity and alcohol-related problems, but this effect was not significant (Cohen’s d = 0.16, p = .13). Generally, at higher levels of between-person PTSD severity, there was a weaker association between quantity of alcohol consumed and self-rated alcohol-related problems.
Figure 1. Plot of modeled effects on alcohol-related problems by number of drinks consumed and between-person overall PTSD severity. Low and high PTSD scores were derived by calculating 1 SD offsets from the mean. Effect sizes and corresponding p values reflect contrasts between mean PTSD symptom levels and 1 SD offsets. PTSD = posttraumatic stress disorder; GMS = grand-mean standardized.
DiscussionThis study examined the daily effects of PTSD severity and alcohol consumption on alcohol-related problems among treatment-seeking men and women with co-occurring PTSD and alcohol dependence. Consistent with prior research (Gaher et al., 2014), we found that daily variability in PTSD severity, overall between-person PTSD severity, and alcohol consumption were all broadly predictive of alcohol-related problems. Our research extends these findings by providing evidence for moderating effects of both overall PTSD severity and daily PTSD symptom variability on self-appraised alcohol-related problems. We found that on nondrinking days and moderate drinking days (three drinks consumed), greater daily within-person PTSD symptom severity was associated with greater alcohol-related problems. Additionally, compared with participants with lower overall between-person PTSD scores, participants with higher overall PTSD severity reported higher ratings of alcohol-related problems on nondrinking and moderate-drinking days. This pattern was not apparent, however, on heavier drinking days, when neither within- nor between-person PTSD were associated with degree of alcohol-related problems. Given the strength of the moderating effect of between-person PTSD, these results suggest that for those with more severe PTSD there is a less contingent association between how much they drink on a given day and the extent to which they report negative consequences from drinking on nondrinking and moderate-drinking days.
These results may help to shed light on the prior research indicating that treatment-seeking individuals with comorbid PTSD and AUD, compared with those with AUD only, report greater alcohol dependence severity despite comparable or lower levels of consumption (Fuehrlein et al., 2014; Petrakis et al., 2006). In the present study, the weaker association between consumption and alcohol-related problems among those with higher levels of PTSD may be due in part to the bidirectional relationship between PTSD and negative coping strategies (Read, Griffin, Wardell, & Ouimette, 2014), suggesting that those with higher levels of PTSD may engage in negative coping strategies that lead to negative alcohol-related experiences regardless of quantity of alcohol consumed. Similarly, individuals with high levels of PTSD may experience negative effects from even small amounts of alcohol given positive associations between impulsivity/emotion dysregulation, and problematic alcohol use among those with PTSD (Schaumberg et al., 2015; Tripp & McDevitt-Murphy, 2015). It is noteworthy that among individuals with high levels of PTSD, the mean score for alcohol-related problems fell in the middle of the scale, suggesting that the weaker association between alcohol use and problems cannot be explained by a measurement ceiling effect.
It is also possible that alcohol craving or abstinence effects (i.e., withdrawal, hangover) were particularly detrimental to participants with higher PTSD on days with low or no alcohol use. Prior research has shown that the relationship between PTSD and alcohol-related consequences is mediated by alcohol craving (Tripp et al., 2015), and that exposure to trauma cues is linked to increased alcohol craving (Coffey et al., 2002). If those with higher PTSD have higher levels of craving on low- or nondrinking days, this could explain their self-rating of alcohol problems during days when they are not drinking as heavily. Indeed, both overall PTSD severity and daily increases in PTSD symptoms were associated with moderately high ratings of alcohol-related problems on nondrinking days. Additionally, experiences of hangover or alcohol withdrawal may be more aversive for those with severe PTSD, or conversely may temporarily exacerbate within-person PTSD symptoms.
Additionally, individuals with severe PTSD often have cognitive biases that may affect their accurate appraisal of alcohol-related problems. A core component of the disorder is a tendency to view experiences in a negative light and appraise situations as inherently dangerous (Vythilingam et al., 2007). Additionally, those with PTSD show memory bias toward trauma- and threat-related stimuli (Paunovic, Lundh, & Öst, 2002). Thus, a person with higher PTSD symptoms may recall a relatively minor incident that occurred after a single drink (e.g., an argument) as more distressing or problematic than someone with lower levels of PTSD. Similarly, this person may find it difficult to distinguish or attend accurately to the nuances between distinct incidences of varying importance or extremity, thus remembering them all with a similar valence. Additionally, given some particularly high responses with regards to drinking quantity, it is possible that memory bias also affected reporting of drinking patterns.
An interesting find was that the results indicated that between-person differences in PTSD more strongly moderated the association between alcohol consumption and problems than intraperson variability in PTSD. One possible explanation for this result is that the individual variability in PTSD severity was low. However, it is also possible that compared with daily PTSD, overall PTSD severity is a more sensitive marker for the negative impact of PTSD on functioning and perceived effects of alcohol use. This second explanation makes sense in the context of evidence for latent classes of PTSD that differ based upon overall, chronic symptomatology (e.g., Cloitre, Garvert, Weiss, Carlson, & Bryant, 2014; Galatzer-Levy, Nickerson, Litz, & Marmar, 2013).
Amplified alcohol-related problems on nondrinking and moderate-drinking days among those with more severe PTSD could be an important factor in treatment for individuals with comorbid PTSD and AUD. For example, individuals with more severe PTSD who overestimate alcohol-related problems may feel greater motivation to seek out and adhere to treatment. On the other hand, this overestimation may lead to lower self-efficacy for treatment success, which could partially explain the poor treatment outcomes often seen for this group (Saxon & Simpson, 2015). Additionally, this pattern of highly rated drinking problems on low-drinking days could be symptomatic of attentional biases that interfere with the accurate self-assessment that contributes to successful PTSD and AUD treatment (Donovan, 2003; Prochaska & Velicer, 1997). In other words, if a patient’s attentional bias causes them to consistently overestimate the role of alcohol in their life problems, this may interfere with their ability to make and achieve behavior change goals. Ultimately, treatment to address both PTSD and AUD may require interventions that support accurate assessment of drinking consequences either to increase motivation to engage in treatment or to clarify the impact of treatment as it (and its corresponding gains) occurs, or both.
These results should be viewed in the context of some study limitations. First, findings from this study were specific to a treatment-seeking sample, who may already be making efforts to reduce alcohol consumption. Thus, findings may not generalize to individuals with comorbid PTSD/AUD who are not seeking treatment. Additionally, all measures were self-report, which increases the likelihood of misinterpretation and other biases. Also, alcohol-related problems were assessed with a single item that asked about participants’ subjective experience of problems rather than the number of problems or specific types of problems. Given that this measure did not specify whether problems were because of active use or because of withdrawal, a more detailed measure of alcohol-related problems should be included in future studies. Measures also did not include consumption of other illicit substances, such as marijuana, opiates, cocaine, and methamphetamines. In addition, though only 1-day prior, retrospective reporting of PTSD symptoms, alcohol use, and problems, may have resulted in recall bias. However, questioning respondents at the end of the day would have increased the risk of missing consumption data and alcohol-related problems occurring later in the evening or early morning. In addition, we were only able to monitor participants for 1 week before receipt of a brief intervention; future research should use a longer monitoring period to better assess within-person changes and to determine whether these findings are maintained across time. Furthermore, given our sample of treatment seeking individuals with comorbid PTSD and alcohol dependence, results must be generalized as such. However, a strength of the study is that the sample included both veterans and civilians with almost equal distribution across gender. Finally, future research should include comparison groups with other psychological disorders or alcohol dependence only to parse out the influence of PTSD-specific symptomatology.
Despite these limitations, the results of the present study could provide guidance for future investigations on this important topic, including examination of potential mediators. For instance, cognitive symptoms such as catastrophizing or negative rumination may explain the weaker association between consumption and problems for those with more severe PTSD, as discussed above. Alternatively, negative coping processes (e.g., avoidance and anger reactivity) that are common components of PTSD may be contributing to this effect by compounding or prolonging alcohol’s effects. Additionally, gender may also moderate the effect of alcohol consumption and PTSD symptoms on self-ratings of alcohol-related problems. Further research should include consideration of coping mechanisms to increase our understanding of alcohol use and treatment in the context of PTSD.
Footnotes 1 Previously published findings from this dataset showed modest lagged effects of posttraumatic stress disorder (PTSD) on next-day number of drinks (Simpson et al., 2014). In the current study, additional models not presented in current results demonstrated that there were no lagged effects of PTSD on alcohol-related problems. We ran models of lagged effects of PTSD on alcohol-related problems (controlling for same-day PTSD) and found no lagged effects of intraindividually varying PTSD symptoms on alcohol-related problems in either the logistic (p = .90) or gamma models (p = .86).
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Submitted: October 8, 2015 Revised: November 1, 2016 Accepted: November 3, 2016
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Source: Psychology of Addictive Behaviors. Vol. 31. (1), Feb, 2017 pp. 27-35)
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Record: 19- Depressive symptoms, religious coping, and cigarette smoking among post-secondary vocational students. Horton, Karissa D.; Loukas, Alexandra; Psychology of Addictive Behaviors, Vol 27(3), Sep, 2013 pp. 705-713. Publisher: American Psychological Association; [Journal Article] Abstract: Depressive symptoms are associated with increased levels of cigarette smoking, yet not every individual experiencing depressive symptoms smokes. This study examined whether religious coping moderated the impact of depressive symptoms on past 30-day cigarette use among a racially/ethnically diverse sample of 963 postsecondary vocational students (46.8% women; mean age = 25 years). Results from negative binomial regression analyses indicated that depressive symptoms increased the likelihood of cigarette smoking (quantity−frequency measure of cigarette use) for female students, whereas positive religious coping decreased the likelihood of smoking for female students. Consistent with religious coping theory and as expected, negative religious coping moderated the depressive symptoms-smoking relationship such that negative religious coping exacerbated the impact of depressive symptoms on cigarette smoking among females. Positive religious coping also moderated the depressive symptoms-cigarette smoking relationship for females. However, contrary to expectations, high levels of positive religious coping exacerbated the likelihood of cigarette smoking among females with high levels of depressive symptoms. Surprisingly, neither depressive symptoms nor positive or negative religious coping contributed to the likelihood of males’ smoking. Study limitations and suggestions for directions in future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Depressive Symptoms, Religious Coping, and Cigarette Smoking Among Post-Secondary Vocational Students
By: Karissa D. Horton
Department of Kinesiology and Health Education, The University of Texas at Austin
Alexandra Loukas
Department of Kinesiology and Health Education, The University of Texas at Austin;
Acknowledgement: Karissa D. Horton is now the Cofounder and Principal Consultant at Limetree Research, LLC.
This research was supported by Grant #R03CA130589 from the National Cancer Institute, awarded to the second author, and an American Association for Health Education Will Rogers Institute Fellowship, awarded to the first author.
Although the rate of cigarette smoking by adults in the United States is now half of what it was in 1964 (U.S. Department of Health, 1964), 19.3% of U.S. adults 18 years and older report that they smoke cigarettes (Schiller, Lucas, Ward, & Peregoy, 2012). Tobacco use remains the leading cause of preventable death in the U.S. (Mokdad, Marks, Stroup, & Gerberding, 2004) and individuals dealing with depressive symptoms tend to smoke more than their nondepressed peers (Anda et al., 1990; Kenney & Holahan, 2008). A study of the historical trends of the relationship between depression and cigarette smoking suggests that the depression−cigarette use relationship has only become apparent in recent years as the prevalence rate of cigarette smoking declined and those who continue to smoke are more likely to be depressed (Murphy et al., 2003).
Depressive symptoms are a significant cause of stress with the potential to spin out of control if an individual does not engage in the necessary efforts to cope with their situation (Smith, McCullough, & Poll, 2003). Tomkins’ (1966) negative affect model for smoking suggests that some may smoke to reduce their negative affect enough to deal with and get to the root of their stress, whereas others may use cigarettes to completely sedate themselves without facing their stressors. Perhaps as Murphy et al. (2003) noted, the relief associated with the reduction of depressive symptoms is so beneficial that depressed individuals continue to smoke cigarettes despite the well-known harmful health effects. Yet, not every individual who experiences depressive symptoms smokes, suggesting that other variables may moderate the relationship between depressive symptoms and cigarette use. Therefore, this study examined the unique role of religious coping, over and above nonreligious coping, as a potential moderator of the relationship between depressive symptoms and cigarette use among a racially/ethnically diverse sample of students enrolled in 2-year postsecondary vocational schools.
The more religiously involved an individual is, the less likely she or he is to smoke cigarettes (see Koenig, McCullough, & Larson, 2001). This association has been reported in samples of college students (Oleckno & Blacconiere, 1991), young adults (Whooley, Boyd, Gardin, & Williams, 2002), and older adults (Koenig et al., 1998). Although these studies indicate an inverse relationship between the institutional aspects of religious involvement (e.g., religious service attendance) and cigarette use (see Koenig et al., 2001), relatively less research has examined the unique role of more personal measures of religiousness, such as religious coping, on cigarette use. Nonetheless, drawing on religious coping theory and existing research (Koenig et al., 2001), it is likely that individuals who use positive religious coping may turn to a “divine other” (e.g., God of monotheistic faiths) for support in times of stress and in this way reduce reliance on smoking, whereas those who engage in negative religious coping may not only lack that sense of support but feel a sense of abandonment that increases the possibility of smoking. From this perspective positive religious coping should be directly inversely associated with smoking, whereas negative religious coping should be positively associated with smoking.
Religious CopingReligious coping can be defined as specific means through which some individuals incorporate “a full range of behaviors, emotions, cognitions, and relationships” to deal with a variety of stressful situations (Pargament, Tarakeshwar, Ellison, & Wulff, 2001, p. 498). Positive religious coping includes a search for a spiritual connection, a collaborative relationship with a divine other, and seeking support from a divine other (Pargament, 1999). Conversely, negative religious coping includes a sense that a divine other is punishing oneself for sins as well as a sense of abandonment by a divine other (Pargament, 1999). A variety of health-related outcomes associated with positive and negative religious coping vary in expected ways. For example, positive religious coping is associated with better stress-related adjustment compared with negative religious coping, which is associated with more adjustment problems (Pargament, Smith, Koenig, & Perez, 1998). Personal measures of religiousness may play a particularly salient role for individuals who experience decreased levels of control in their life. Prayer, for example, is a common coping strategy that individuals draw upon when dealing with stress (see Pargament, 1997).
Religious Coping as a Moderator of Depressive Symptoms and Cigarette UseIn addition to the direct effects of positive and negative religious coping on cigarette use, we anticipate that both types of religious coping will moderate the association between depressive symptoms and current cigarette use. Feeling a sense of support from a divine other (positive religious coping) may buffer, or lessen the deleterious impact of depressive symptoms on cigarette use. Depressed individuals are more likely than nondepressed individuals to become socially withdrawn (see review by Tse & Bond, 2004), exhibit excessive reassurance seeking (Joiner & Metalsky, 1995), and express neediness for emotional support. Coyne’s (1976) interactional theory of depression suggests that the increased demand on these relationships often leads to diminished levels of support for, and even rejection of, the depressed individual (see Joiner & Metalsky, 1995). Thus, individuals lacking in social support from their “real” relationships may particularly gain from the support, and the sense of control over stressful situations, provided by a collaborative relationship with a perceived divine other. This relationship with a divine other may rival the intensity of relationships with family and friends (Pollner, 1989). As noted by Pollner (1989, p. 93), the divine is often personified as one who will convey a sense of support as well as guidance for individuals, particularly during stressful circumstances. A supportive relationship with a divine other may diminish a depressed individual’s need to smoke cigarettes by filling the void of social support that those with depressive symptoms often experience. In this way, a relationship with a divine other may make up for a lack of relationship or support from family and friends.
Unlike the buffering role of positive religious coping, negative religious coping may exacerbate the influence of depressive symptoms on cigarette smoking. Unsupportive social relationships coupled with feelings of abandonment and/or punishment by a divine other may decrease one’s sense of purpose and meaning in life and worsen existing feelings of negative affect, all of which may increase cigarette smoking among those with depressive symptoms. For example, compared with their peers who reported lower levels of negative religious coping, individuals living with HIV/AIDS who reported higher utilization of negative religious coping strategies subsequently reported significantly higher levels of depressive symptoms and more symptoms associated with HIV/AIDS (Trevino et al., 2010).
Occupational Status and Sense of Control Through a Divine OtherIndividuals in blue-collar occupations, which rank low on occupational prestige, tend to lack autonomy in their daily work and a sense of control at their job, which increase their risk for experiencing depressive symptoms (Link, Lennon, & Dohrenwend, 1993). These individuals are also more likely than white-collar workers to have higher rates of current smoking (Barbeau, Krieger, & Soobader, 2004). Blue-collar workers may lack the coping resources typically afforded only to those with higher levels of socioeconomic status and thus, may possibly gain a sense of control through a relationship with a divine other (Pollner, 1989). In this way, religious coping could be a coping resource that fills a specific need for blue-collar workers who perceive a lack of control in their life. Vocational students enrolled in postsecondary 2-year vocational/technical school programs, which prepare students for blue-collar occupations (e.g., welding, air-conditioning), also report higher smoking rates than the general adult population (Loukas, Murphy, & Gottlieb, 2007), making vocational students an excellent population for the current study.
Gender Differences Across Study VariablesA discussion of depressive symptoms, cigarette smoking, and religious coping would not be complete without examination of gender differences. Women are twice as likely as men to suffer from depressive symptoms (Nolen-Hoeksema, 2001) and women report more cigarette smoking in response to negative affect (Brandon & Baker, 1991). It follows, then, that depressive symptoms may predict women’s smoking outcomes to a greater degree than men’s. Women report more religious involvement than men across a variety of indicators (see Beit-Hallahmi & Argyle, 1997). Likewise, women’s stronger propensity to rely upon and cultivate relationships with others, including a divine other (Pollner, 1989), increases the likelihood that the direct and moderating influence of religious coping will be greater for them compared with men. Moreover, societal norms tend to suggest that women are more reliant upon others to deal with the stressors they face, whereas men are expected to be more self-reliant when working through their problems. So, in this sense, women may be more likely than men to seek out, engage in, and benefit from a collaborative relationship with a divine other, whereas men may be more likely to deal with their problems without such a reliance on a divine other (Maynard, Gorsuch, & Bjorck, 2001).
Study HypothesesFindings in the literature indicate that religious and nonreligious coping are correlated and that even among those who draw on religious coping to deal with stress, nonreligious coping is also a fundamental part of their coping repertoire (Pargament, 1997). To provide a conservative test of the impact of religious coping on smoking, the overlapping variance associated with nonreligious problem-focused (active coping, use of instrumental social support) and emotion-focused coping (denial, self-blame) was removed. Thus, we tested whether or not religious coping makes a unique contribution to cigarette smoking over and above the contribution of nonreligious coping. Moreover, because age and financial stress are positively correlated with cigarette smoking (Siahpush, Borland, & Scollo, 2003) and because cigarette smoking outcomes vary by race (Kiviniemi, Oromo, & Giovino, 2011), this study also controlled for these variables. In summary, the current study examined whether positive and negative religious coping moderated the relationship between depressive symptoms and the likelihood of current cigarette use, over and above the contributions of age, race, financial stress, and nonreligious problem- and emotion-focused coping. We hypothesized that:
1. Depressive symptoms, negative religious coping, and emotion-focused nonreligious coping would be directly associated with higher levels of past 30-day cigarette use, and positive religious coping and problem-focused nonreligious coping would be directly associated with lower levels of current cigarette use.
2. Positive religious coping would buffer or lessen the impact of depressive symptoms on past 30-day cigarette use, whereas negative religious coping would exacerbate or worsen the negative impact of depressive symptoms on past 30-day cigarette use.
Method Participants
Study participants were drawn from a larger study comprised of a convenience sample of 1,120 students recruited from 81 required introductory- and advanced-level classes at two 2-year public colleges in Texas. Student enrollment at the participating schools was 2,590 and 9,582, respectively. The schools offer programs in areas including automotive technology, air-conditioning repair, allied health, business management, computer-aided drafting, medical technology, repair and manufacturing technology, and vocational nursing. Of the 1,434 students enrolled in the 81 classes, 1,131 were in class during the survey administration and 1,120 (78% of enrolled students) volunteered to complete the 117-item anonymous self-report Vocational Student Tobacco Use Survey. Students with any missing data were excluded from the analyses (n = 157); thus, the final sample size was 963.
Of the 963 students who had complete data, 46.8% were women, 53.2% were men; 38.5% were White, 23.9% were African American, 32.8% were Hispanic, 4.7% reported another race/ethnicity, and 10 participants did not provide their race/ethnicity. With regard to age (M = 25.30, SD = 8.59) 60.6% of the participants were aged 18 to 24, 25.6% were 25 to 34, 13.8% were older than 35, and 11 participants did not complete this item. Although respondents did not provide their religious affiliation, county-level data assessed in 2000 indicated that in the first county where data were collected religious affiliation was: Evangelical Protestant, 29.9%; Catholic, 25%; Mainline Protestant, 8.4%; Other; 1.1%; Orthodox, 0.2%; and Unclaimed, 35%. In the second county, religious affiliation was: Catholic, 41.2%, Evangelical Protestant, 16%; Mainline Protestant, 6.1%; Other, 1.8%; Orthodox, .09%, Unclaimed, 34.9% (Association of Statisticians of American Religious Bodies, 2000).
Procedure
Upon approval from the Institutional Review Boards (IRBs) at the University conducting the study and the participating college with an IRB, the researchers scheduled administration of the voluntary and anonymous survey during 25 minutes of class time. A total of 40 classes participated in data collection at the first school (30 classes fall 2007, 10 classes spring 2008). At the second school, a total of 41 classes (20 classes fall 2007, nine classes spring 2008, 12 classes summer 2008) participated in data collection. Across both schools, the survey was administered in 81 classes, from fall 2007 to summer 2008.
Measures
Demographic covariates
Students reported basic demographic information including gender, age, and race/ethnicity. One item from Pearlin, Menaghan, Lieberman, and Mullan’s (1981) measure of economic strain was used to assess students’ perceived financial stress. This item measured students’ financial situation “at the end of the month” (1 = some money left over; 2 = just enough to make ends meet; 3 = not enough money to make ends meet). A high score indicates higher levels of perceived financial stress.
Nonreligious coping covariates
Eight items (four subscales) from the 28-item Brief COPE (Carver, 1997) were used to assess how often students engaged in four different types of nonreligious coping strategies. Students were asked to “think about how you try to understand and deal with major problems in your life. To what extent is each involved in the way you cope?” The four types of nonreligious coping were measured with two items each. Two items measured active coping (e.g., “I’ve been concentrating my efforts on doing something about the situation I’m in.”); two items measured instrumental support (e.g., “I’ve been trying to get advice or help from other people about what to do.”); two items measured denial (e.g., “I’ve been refusing to believe that it has happened.”); and two items measured self-blame (e.g., “I’ve been blaming myself for things that happened.”). Each item was scored on a scale from 0 to 3 and a high score indicated greater use of a coping strategy. Based on an exploratory factor analysis and previous research (Carver, Scheier, & Weintraub, 1989), two common types of nonreligious coping—problem-focused (active coping, using instrumental support) and emotion-focused (denial, self-blame) coping—served as control variables in this study. In this sample, Cronbach’s alpha for problem-focused coping was 0.87 and 0.84, for women and men, respectively. Cronbach’s alpha for emotion-focused coping was 0.85 and 0.87, for women and men, respectively.
Religious coping
Five items were selected from the 6-item Brief RCOPE (Pargament, 1999) to assess how often (0 = a great deal to 3 = not at all) students engage in both positive and negative forms of religious coping when dealing with a major life problem. Prior to answering the religious coping items, students were asked to “think about how you try to understand and deal with major problems in your life. To what extent is each of these involved in the way you cope?” Three items measured positive religious coping (e.g., “I work together with God as partners to get through the hard times.”) and two items measured negative religious coping (e.g., “I feel that stressful situations are God’s way of punishing me for my sins or lack of spirituality.”). The self-directing negative religious coping item (“I try to make sense of the situation and decide what to do without relying on God.”) is considered to be conceptually distinct from the other negative religious coping items and thus, was removed. That is, whereas the two remaining negative religious coping items consider the individual’s relationship with God, the self-directing negative religious coping item taps into the individual’s actions apart from a relationship with God and in this way may be more similar to other nonreligious coping strategies. Each item was reverse coded so that higher scores indicate greater use of positive and negative religious coping strategies.
Although past research studies generally report good reliability for positive religious coping (e.g., α = .81), Cronbach’s alpha tends to be lower for negative religious coping (e.g., α = .58; see Pargament et al., 2001). Consistent with Pargament’s study, the Cronbach’s alpha for the 3-item positive religious coping subscale was 0.86 and 0.90, for women and men, respectively, whereas Cronbach’s alpha for the 2-item negative religious coping subscale was 0.59 and 0.65, for women and men, respectively.
Depressive symptoms
The Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) is a 20-item measure of the frequency and severity of depressive symptoms occurring in the past week. Students indicated how often in the past week they experienced symptoms of the following four subscales: somatic complaints, depressive affect, positive affect, and interpersonal problems. Each item (e.g., “I felt that I could not shake off the blues even with help from my family or friends.”) is scored on a scale ranging from 0 = rarely or none of the time; less than 1 day to 3 = most or all of the time; 5 to 7 days; therefore, a high score indicates elevated levels of depressive symptomatology. Previous studies have validated the four-factor structure of the CES-D in adult samples (Golding & Aneshensel, 1989) and provided evidence of reliability for this well-established measure of depressive symptoms. Cronbach’s alpha for the CES-D measure was 0.85 and 0.83 for women and men, respectively, indicating that the measure has good internal consistency reliability in the current sample.
Quantity−frequency of current cigarette use
Current cigarette use was measured using two items from the National College Health Risk Behavior Survey (Centers for Disease Control & Prevention, 1997). One item measured frequency of current cigarette use (e.g., “During the past 30 days, on how many days did you smoke cigarettes?”). Students identified how often they smoke cigarettes on a scale ranging from 0 = 0 days to 6 = all 30 days. One item measured quantity of current cigarette use (e.g., “During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day?”). Students identified the quantity of current cigarette use on a scale ranging from 0 = I did not smoke cigarettes during the past 30 days to 6 = more than 20 cigarettes per day. Similar to Schleicher, Harris, and Catley (2009), the two current cigarette use items were multiplied together to create a quantity−frequency variable for cigarette use, which provided a count of the quantity−frequency of cigarettes smoked during the 30 days prior to the survey and ranged from 0 to 36.
Missing Data Analysis
Data from 157 students were removed from the current sample because they were missing substantial amounts of data. A series of analyses were conducted to examine if the data from these 157 students differed from the 963 that were retained for study analyses, potentially resulting in an exclusion bias. The nonparametric Mann–Whitney U test indicated that the distribution of scores on the quantity−frequency of cigarette smoking variable did not differ between the two groups (p = .87). Similarly, independent samples t tests indicated that there were no differences between the two groups on positive religious coping [t(1040) = 1.79, p = .08], negative religious coping [t(1038) = 1.17, p = .24], or depressive symptoms [t(1083) = 1.42, p = .16]. Thus, removal of 157 students’ data from the current sample did not result in exclusion bias.
Data Analysis
Negative binomial regression was used to examine the unique contributions of positive and negative religious coping to the quantity−frequency of past 30-day cigarette use after accounting for the contributions of the covariates (age, race, perceived financial stress, problem- and emotion-focused coping). Negative binomial regression is the preferred statistical method when the dependent variable represents a count of events that are non-normally distributed (Hox, 2010). In the present study, 70.2% of participants received a 0 (indicating they were nonsmokers) on the quantity−frequency variable; thus, this count variable was non-normally distributed.
To determine if the association between depressive symptoms and current cigarette use varied (was moderated) by use of positive and negative religious coping, two separate two-way interactions between depressive symptoms and religious coping were tested. In order to determine the nature of a significant interaction, the methods outlined by Aiken and West (1991) were used. Specifically, the relationship between depressive symptoms and quantity−frequency of past 30-day cigarette use was examined at high (1 SD above the mean value) and low (1 SD below the mean value) levels of the positive or negative religious coping variable. In order to prevent problems with multicollinearity, the nonreligious coping (problem- and emotion-focused) covariates and the depressive symptoms and religious coping (positive and negative) predictor variables were each mean-centered (Aiken & West, 1991).
Each of the covariates (age, race, financial stress, problem- and emotion-focused coping) and all three predictor variables (depressive symptoms, positive and negative religious coping) were entered simultaneously in model one. Both of the two-way interaction terms (depressive symptoms x positive religious coping; depressive symptoms x negative religious coping) were tested independently in model two, with all covariates and main effects included.
Results Preliminary Analyses
Given that the comorbidity of cigarette smoking and depression vary across males and females (Husky, Mazure, Paliwal, & McKee, 2008), Box’s M test was used to determine the homogeneity of variance−covariance matrices of the study variables across gender in this study. Because Box’s M test was significant (Box’s M = 58.67, df = 36, p = .01) indicating that the variance−covariance matrices varied across gender, all subsequent models were run separately for male and female students.
Mean Differences Across Gender
Mean differences across gender in the study variables were examined for descriptive purposes and are presented in Table 1. With the exception of age, race, and emotion-focused nonreligious coping, results indicated a statistically significant difference at the p < .05 level in scores for each study variable across gender. Female students reported significantly more perceived financial stress, depressive symptoms, positive religious coping, and problem-focused coping than did male students. However, male students reported significantly more negative religious coping and quantity−frequency of past 30-day cigarette use than did female students.
Means, Standard Deviations, and Zero-Order Correlations (R) for Male (n = 512) and Female (n = 451) Students’ Study Variables
Zero-Order Correlations
Prior to testing the study hypotheses, zero-order correlations between the independent and dependent variables for males and females were examined. As shown in Table 1, compared with their non-White peers, both White male and White female students were more likely to smoke cigarettes in the past month. Perceived financial stress, depressive symptoms, and problem-focused nonreligious coping were each associated with increased smoking among female students. Lastly, positive religious coping was associated with decreased cigarette smoking in the past month among female students.
Main Effects
The negative binomial regression, for model one, predicting quantity−frequency of past 30-day cigarette use from the covariates, depressive symptoms, and positive and negative religious coping was statistically significant for male, χ2(8) = 49.08, p < .001, and female, χ2(8) = 159.27, p < .001, students (see Table 2, model one). Among the covariates, age and race were both uniquely associated with past 30-day cigarette use for male and female students, whereas financial stress and emotion-focused coping were uniquely associated with past 30-day cigarette use, but only for female students. Among the predictor variables, depressive symptoms and positive religious coping were significantly associated with quantity−frequency of past 30-day cigarette use, but for females only.
Negative Binomial Regression Analysis for Variables Predicting Quantity-Frequency of Past 30-Day Cigarette Use for Male (n = 512) and Female (n = 451) Students
Given that the negative binomial regression model is log-linear, it is possible to convert the regression coefficients into the predicted multiplicative effect of a 1-unit change in the variable of interest (i.e., depressive symptoms) on the count of cigarette use, holding all other variables constant (Coxe, West, & Aiken, 2009). For example, the exponentiation of the regression coefficient for financial stress (e0.37 = 1.45) indicates the multiplicative difference in quantity−frequency of cigarettes smoked based on a female student’s financial stress. Thus, a female student with a perceived financial stress score of 3 is expected to have a quantity−frequency score for cigarettes smoked that is, on average, 1.45 times greater than a student with a perceived financial stress score of 2. Examination of the exponentiation of the regression coefficients for the other covariates indicate that for both males and females, a student who is 25-years-old is expected to have a quantity−frequency score that is, on average, 1.02 times greater than a 24-year-old student of the same gender and that White male and female students are expected to have quantity−frequency scores that are, on average, 1.92 and 2.61 times greater than their non-White counterparts, respectively. In addition, a female with an emotion-focused coping score of 9 is expected to have a quantity–frequency score for cigarettes smoked that is, on average, 1.05 times greater than a female with an emotion-focused coping score of 8.
Regarding the exponentiation of the regression coefficients for the female depressive symptoms and positive religious coping predictor variables (see Table 2), results indicate that a student with a depressive symptoms score of 20 is expected to have a quantity−frequency score for cigarettes smoked that is, on average, 1.03 times greater than a student with a depressive symptoms score of 19. Furthermore, a 1-unit increase in positive religious coping causes the expected quantity−frequency score for cigarettes smoked by female students to decrease by a factor of 0.93.
Interaction Effects
As shown in Table 2, model two, four two-way interactions were significant: two between depressive symptoms and positive religious coping, one for males, χ2(9) = 57.08, p < .001, and one for females, χ2(9) = 167.80, p < .001, and two between depressive symptoms and negative religious coping, one for males, χ2(9) = 55.01, p < .001, and one for females, χ2(9) = 171.87, p < .001. Probing the significant 2-way interaction between depressive symptoms and negative religious coping for female students revealed that depressive symptoms were significantly associated with cigarette smoking during the past month at high level of religious coping (β = .05, p < .001), but not low levels of religious coping (β = .01, p > .05; see Figure 1). As expected, findings indicated that negative religious coping exacerbated the influence of depressive symptoms on the likelihood of female students’ past 30-day cigarette use. On the other hand, probing male students’ depressive symptoms x negative religious coping interaction revealed that depressive symptoms were not associated with quantity−frequency of past 30-day cigarette smoking at high levels of negative religious coping (β = .003, p > .10), but were marginally associated with past month smoking at low levels of negative religious coping (β = −02, p = .086). Given the nonsignificant and marginal associations, this finding is not discussed further.
Figure 1. Examining the depressive symptoms × Negative Religious Coping interaction for female students.
Probing the significant depressive symptoms x positive religious coping interaction for female students indicated that depressive symptoms were significantly associated with quantity−frequency of past 30-day cigarette use at high levels of positive religious coping (β = .05, p < .001), but not low levels of positive religious coping (β = .003, p > .05). Although unexpected, these results indicate that high levels of positive religious coping exacerbated the influence of depressive symptoms on the likelihood of females’ current cigarette use. However, examination of the point estimates in Figure 2 indicate that high levels of positive religious coping may be protective for female students reporting low levels of depressive symptoms, but the protective effect is no longer present for those females with high levels of depressive symptoms. For males, probing the depressive symptoms x positive religious coping interaction revealed that depressive symptoms were marginally associated with quantity−frequency of past month cigarette use at high (β = .02, p = .08) and low (β = −.02, p = .07) levels of positive religious coping. Given these findings, this interaction is not further discussed.
Figure 2. Examining the depressive symptoms × Positive Religious Coping interaction for female students.
DiscussionDespite the growing amount of research examining the influence of religious involvement on various health behaviors, our knowledge of the influence of religious coping on the association between depressive symptoms and cigarette use is lacking. This study extends existing research by examining religious coping as a moderator of the relationship between depressive symptoms and the likelihood of past 30-day cigarette use, while controlling for the influence of nonreligious coping and other covariates in a racially/ethnically diverse sample of vocational/technical school students. In partial support of Hypothesis 1, findings indicated that depressive symptoms were associated with an increased likelihood of cigarette use among female students, whereas positive religious coping decreased the likelihood of female students’ cigarette use, even after controlling for demographic variables and nonreligious coping. Corroborating theory on the role of religious coping, results also showed that negative religious coping exacerbated the influence of depressive symptoms on the likelihood of cigarette use for females. Unexpectedly, however, positive religious coping also exacerbated the influence of depressive symptoms on the likelihood of female cigarette use.
Consistent with Tomkins’ (1966) negative affect model for smoking and prior research (Kenney & Holahan, 2008), depressive symptoms were associated with an increased likelihood of current cigarette use, but only among female students. Past studies report that women have higher rates of depression/depressive symptoms than men (see Nolen-Hoeksema, 2001) and are more likely to use tobacco when dealing with feelings of negative affect (Brandon & Baker, 1991). Moreover, this finding supports those of others showing that depression (Husky et al., 2008) is more strongly associated with cigarette smoking in women than men. The positive association between depressive symptoms and smoking lends itself to the paradox of coping and substance (cigarette) use noted by Brandon, Herzog, Irvin, and Gwaltney, 2004: cigarette smoking functions both as a way to deal with one’s stress and a deleterious health outcome associated with other unproductive efforts to deal with stress.
Additional findings indicated that in comparison with their counterparts, female vocational students who used positive religious coping were less likely to report current cigarette use. Just as Tomkins’ (1966) negative affect model for smoking suggests that some smoke to relieve their stress, findings from this study suggest that female vocational students may use positive religious coping to deal with their stress, but without the health risks of smoking. It is likely that individuals who see themselves as working together with a divine other to deal with their problems may be able to better adapt to their stressful situations (see review by Koenig et al., 2001). These findings add to the growing literature showing the beneficial influence of various aspects of religion on females’ health behaviors (Koenig et al., 2001).
Contrary to the findings for positive religious coping, negative religious coping was not directly associated with past 30-day smoking. However, results confirmed our expectations that negative religious coping would interact with depressive symptoms and exacerbate its impact on the likelihood of current cigarette use. Specifically, female students who reported high levels of depressive symptoms and also engaged in high levels of negative religious coping had an increased likelihood of current cigarette use. Individuals who experience depressive symptoms often exhibit negative emotions and behaviors that turn others away from them, eroding their network of support (Coyne, 1976). Therefore, female students who have strained social relationships associated with depressive symptoms may also feel that God has abandoned them and that their stress is a result of God punishing them. For this reason, female students may be at increased risk for smoking cigarettes.
Unexpectedly, high levels of positive religious coping exacerbated the detrimental impact of depressive symptoms on the likelihood of female students’ current cigarette smoking. In particular, although women with low levels of depressive symptoms may have benefited from the use of positive religious coping, women with high levels of depressive symptoms did not. These findings are partially inconsistent with the direct salutary contribution of positive religious coping to female students’ cigarette use and with our expectation that positive religious coping would buffer the impact of depressive symptoms on current smoking. However, results are consistent with previous findings that positive religious coping exacerbated the influence of perceived racial/ethnic discrimination on the likelihood of African American students’ current cigarette use (Horton & Loukas, 2013). According to Pargament (1997), overreliance on a divine other to assist with or take control of a situation, particularly when an individual needs to take action to deal with their stress, can have harmful consequences. It is possible then that female students with high levels of depressive symptoms may cede personal control over their situation and rely too much on a divine other to get them through difficult situations (Pargament, 1999). Overreliance on positive religious coping to deal with the stress of depressive symptoms may also indicate female students’ inability to assess the best way to deal with their situation. This explanation supports Pargament’s warning of the potential breakdown in the coping process that can occur when the use of religious and nonreligious coping resources are not coordinated appropriately to address a stressful situation (Pargament, 1997). Another explanation for this unexpected finding may be that individuals tend to use all available coping resources, whether positive or negative, to deal with elevated levels of distress associated with depressive symptoms (Coyne, Aldwin, & Lazarus, 1981).
Surprisingly, neither depressive symptoms nor religious coping contributed to the likelihood of males’ smoking. The lack of findings for male students may be due in part to males’ lower mean scores for depressive symptoms and for positive religious coping. The lack of findings for males may also be attributed to the fact that the strength of association between affective disorders and cigarette smoking is weaker for males than females (Brandon & Baker, 1991). Regarding religious involvement, other variables may be better predictors of cigarette smoking for males and should be pursued in future studies. Given that men tend to be less collaborative and less religiously involved than women (Pew Research Center, 2008), subsequent studies should consider the roles of self-directing religious coping (dealing with one’s stress independent of a divine other) and spiritual discontent (feelings of anger and distancing oneself from a divine other) in male vocational school students’ smoking.
The limitations of the current study must be taken into account when interpreting the findings. The cross-sectional study design limits our ability to examine the temporal relationships between the study variables. Future studies should assess both the short- and long-term influences of religious coping on the relationship between depressive symptoms and cigarette smoking using full measures of religious and nonreligious coping. Due to space limitations, a brief form of the religious coping measure and selected subscales of the nonreligious coping measure were used in the present study. Another limitation is the low internal consistency reliability of the 2-item negative religious coping subscale, which may have influenced the study findings. The low internal consistency reliability of negative religious coping may be due to the use of two diverse items measuring struggle with a divine other. Subsequent studies using measures that tap into other more specific aspects of negative religious coping should be conducted to determine if the pattern of findings reported in the present study is replicated. Additionally, the absence of data on the religious affiliation and level of organizational religious involvement of the study participants limits the ability to assess the influence of institutional aspects of religiousness on the study findings. Finally, findings are limited to the examination of religious coping by those affiliated with a monotheistic faith and our measures of religious coping do not include assessment of spiritual coping outside of the influence of organized religion. Future studies should, therefore, examine religious coping for individuals of other faiths and incorporate examination of the role of spirituality in the association between depressive symptoms and smoking.
Notwithstanding these limitations, this study contributes to the literature in several areas, including religious coping, cigarette smoking, and vocational students training to work in blue-collar occupations. This research demonstrated that the direct and moderating influences of religious coping on the likelihood of cigarette smoking occurred in ways that were expected and unexpected. As expected, findings indicated that positive religious coping was associated with a decreased likelihood of female students’ cigarette use and negative religious coping exacerbated the influence of high levels of depressive symptoms on the likelihood of females’ smoking. Although unexpected, this research also suggests that overreliance on positive religious coping increased the likelihood of cigarette smoking for females who reported high levels of depressive symptoms. Simultaneous examination of both the personal (i.e., religious coping, attachment to God) and organizational (i.e., religious affiliation and religious service attendance) measures of religious involvement, depressive symptoms, and cigarette use is needed for a finer-grained analysis of these associations.
There are practical implications to the finding that religious coping is relevant to the female vocational students participating in this study. For females to whom religion is important, religious coping serves as an addition to their repertoire of existing coping resources from which they can draw upon. In particular, such women may also benefit from the social support of those within their religious community and the proscriptive norms of their faith that discourage harmful health behaviors such as smoking (Koenig et al., 2001). The implication of these findings for women who are currently trying to quit smoking, is that in addition to other resources, religious coping may contribute to decreases in smoking.
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Submitted: December 22, 2011 Revised: July 18, 2012 Accepted: October 16, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (3), Sep, 2013 pp. 705-713)
Accession Number: 2012-34896-001
Digital Object Identifier: 10.1037/a0031195
Record: 20- Developing a theory driven text messaging intervention for addiction care with user driven content. Muench, Frederick; Weiss, Rebecca A.; Kuerbis, Alexis; Morgenstern, Jon; Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013 pp. 315-321. Publisher: American Psychological Association; [Journal Article] Abstract: The number of text messaging interventions designed to initiate and support behavioral health changes have been steadily increasing over the past 5 years. Messaging interventions can be tailored and adapted to an individual's needs in their natural environment—fostering just-in-time therapies and making them a logical intervention for addiction continuing care. This study assessed the acceptability of using text messaging for substance abuse continuing care and the intervention preferences of individuals in substance abuse treatment in order to develop an interactive mobile text messaging intervention. Fifty individuals enrolled in intensive outpatient substance abuse treatment completed an assessment battery relating to preferred logistics of mobile interventions, behavior change strategies, and types of messages they thought would be most helpful to them at different time points. Results indicated that 98% participants were potentially interested in using text messaging as a continuing care strategy. Participants wrote different types of messages that they perceived might be most helpful, based on various hypothetical situations often encountered during the recovery process. Although individuals tended to prefer benefit driven over consequence driven messages, differences in the perceived benefits of change among individuals predicted message preference. Implications for the development of mobile messaging interventions for the addictions are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Developing a Theory Driven Text Messaging Intervention for Addiction Care With User Driven Content
By: Frederick Muench
Department of Psychiatry, Columbia University College of Physicians and Surgeons, and Mobile Health Interventions, Department of Psychiatry, Columbia University, New York, NY;
Rebecca A. Weiss
Department of Psychology, Fordham University
Alexis Kuerbis
Research Foundation for Mental Hygiene, Inc., New York, NY, and Department of Psychiatry, Columbia University College of Physicians and Surgeons, Columbia University
Jon Morgenstern
Department of Psychiatry, Columbia University College of Physicians and Surgeons, Columbia University
Acknowledgement: The study was funded through a grant from the National Institute on Drug Abuse (R43 DA029359-01A1; Muench). Frederick Muench was founder of Mobile Health Interventions, which received the SBIR to develop the messaging intervention. This paper describes a treatment development process and not the efficacy of a product or service. Dr. Muench is currently President of Mobile Health Interventions, but the company is in the process of being acquired by a non-profit for which Dr. Muench will remain a consultant. His compensation is fixed and is not dependent on revenue or sales.
The mobile phone is considered by many to be the next frontier in health behavior change because it is a vehicle to deliver personal, intelligent, and adaptive health information anywhere in real time (Patrick, Griswold, Raab, & Intille, 2008; Riley et al., 2011). There is a growing body of research on text messaging for a range of health and mental health problems such as diabetes, asthma, obesity and weight loss, tobacco dependence, bulimia, and even for increasing levels of daily functioning for those with schizophrenia (Cole–Lewis & Kershaw, 2010; Fjeldsoe, Marshall, & Miller, 2009; Heron & Smyth, 2010; Krishna, Boren, & Balas, 2009; Pijnenborg et al., 2010). This is due in part to text messaging or short message service (SMS) being the most accessible and common form of mobile communications. Over 95% of phones are SMS capable, and more text messages are being sent and received every day than mobile phone calls. In addition, although there is a large discrepancy in home Internet access between high and low income households and ethnicities, this is not the case with mobile phone use (Pew Internet & American Life Project, 2010), highlighting that SMS is a particularly useful intervention medium for disenfranchised population groups such as substance users (Fjeldsoe et al., 2009; Heron & Smyth, 2010).
Mobile interventions are particularly well suited for addictions continuing care because they can be utilized with individuals in their natural environment and adapt to their needs in real time using just-in-time therapies. Similar strategies have been used in the empirically supported phone-based continuing care literature (e.g., extended case monitoring, assertive continuing care, recovery management check-ups, early warning signs relapse prevention and concurrent recovery monitoring), which focus on minimal contact over longer periods of time, adapting to the current needs of individuals (McKay, 2009). Despite the promise of SMS interventions for addiction and the encouraging results from tobacco cessation studies (Free et al., 2011), there have been no published studies using SMS for addiction continuing care, nor any studies examining methods to develop effective mobile interventions.
More than other mediums of intervention development, our current behavioral theories may not adequately capture the capabilities of behavior change interventions using mobile phones across disorders. Riley and colleagues (2011) highlight that mobile technology's greatest strength is that it offers the opportunity to use dynamic interactive real-time therapies and that our current behavior change intervention theories, possibly aside from self-regulation theory (Carver & Scheier, 1998), need to be adjusted to account for real-time adaptations at the moment level. Numerous studies have revealed that individuals have very different needs at different times of the change process (Herd & Borland, 2009) and that a range of psychological processes have varying importance throughout the change process (Prochaska & Velicer, 1997; Rothman, Baldwin, & Hertel, 2004). Short message service interventions for smoking cessation have integrated these important just-in-time therapies for participants by providing tailored messages based on participant characteristics and their quit date as well as just-in-time adaptations to care, based on ones current level of functioning (e.g., Brendryen, Kraft, & Schaalma, 2010; Rodgers et al., 2005). Understanding these specific and critical events, as well as in-the-moment decision making, can shape future mobile interventions. With careful understanding, interventions can be individually tailored to each person's needs and specific circumstances.
More than most delivery mediums, mobile interventions are by nature multiple, repeated, brief interventions. Each brief contact can be considered a microintervention to foster a positive cognitive, affective, or behavioral reaction in the moment. Therefore, creative strategies should be used to further develop interventions that move the field toward using these new mediums. For example, SMS interventions can borrow from the large public health literature on tailored benefit driven (gain framed) versus consequence driven (loss framed) messaging, as well as from the decisional balance literature to push specific types of tailored messaging. There is evidence that health messages that are congruent to underlying motivational orientation or personality style are more effective than those that are incongruent (Rimer & Kreuter, 2006; Noar, Benac, & Harris, 2007; Williams–Piehota, Schneider, Pizarro, Mowad, & Salovey, 2004). Additionally, the foundational literature on matching change processes to readiness to change (Prochaska, DiClemente, Velicer, & Rossi, 1993) can be replicated through existing messaging interventions (Brendryen, Kraft, & Schaalma, 2010).
Despite the growing empirical support for mobile interventions targeting general behavioral changes, there is little information on the acceptability of text messaging systems for those with substance abuse problems, particularly for those in addiction treatment. Because addiction carries such stigma, there is a need to understand the acceptability of mobile interventions among the individuals it might serve. This includes understanding what types of messaging are most appropriate; when messages might be perceived as most helpful; what concerns exist regarding using drug-related terms in the intervention content; how many individuals have unlimited messaging plans; and whether individuals are interested in real-time support and social networking as part of mobile interventions. When combined with the lack of research on developing empirically guided mobile content, there is a need to perform foundational research on mobile intervention development for addictive disorders. To help achieve this aim, we performed an exploratory study consisting of a one-time assessment to examine self-reported user acceptance of integrating an interactive SMS system into addiction care. In addition to inquiring about user preferences in relation to messaging logistics, the assessment included questions about the types of messages individuals may prefer receiving (consequence based vs. benefit based). Participants were also asked to generate their own messages in relation to different situations during the change process that they perceived would be most helpful for themselves. These user-generated messages were collected to help develop intervention text messages for specific situations (e.g., treatment entry vs. high craving/risk for relapse). Due to our interest in using messaging as a continuing care intervention for behavioral maintenance, the assessment battery was focused on acceptability, feasibility, and preferences related to messages that would be received in throughout the treatment process, from entry to aftercare. It is important to note that we did not send actual messages to participants but rather obtained this preliminary information through a one-time self-report assessment to eventually develop a pilot continuing care intervention program. The study was approved by the Bronx Lebanon Hospital Institutional Review Board.
Method Participants
Participants included 50 individuals (41 men, 9 women), ranging in age from 26 to 64 years old (mean [M] = 43.5, standard deviation [SD] = 8.1), who were enrolled in intensive outpatient substance abuse treatment at an inner-city clinic. Potential participants were recruited through flyers placed in the treatment program and were instructed to contact their counselor if interested in the study. To be included, participants needed to be fluent in English, read at least at an 8th grade level, be drug free for 30 days and be referred by their primary counselor to participate. Participants were excluded if they were unable to comprehend the consent form quiz, or if their primary counselor reported the presence of a mental or psychiatric disorder that would inhibit completion of the assessment. Three participants were excluded due to language barriers.
Roughly half of the sample was African American (n = 27, 54.0%), 40.0% were Hispanic (n = 20), 4.0% were Caucasian (n = 2), 2.0% were Asian (n = 1), and 2.0% were biracial (n = 1). The majority were unemployed (n = 40; 80.0%), 4% were employed full-time (n = 2), and 4.0% were either employed part-time or students (n = 2), and 12% (n = 6) individuals chose not to respond regarding employment status. Individuals were drug free for an average of 98 days (SD = 104) at the time of the study. Individuals reported the substances that caused the greatest consequences in their lives were alcohol (n = 32; 64%), cocaine (n = 30; 60%), and heroin (n = 18, 32%).
Procedure
Participants who met eligibility criteria were given a copy of the consent form by their primary counselor prior to their attendance at one of four group meetings. Upon arrival at the meeting, the consent form was reviewed, and participants were further screened by the experimenter and given an overview of the 90-minute session. In the group setting, participants independently completed three self-report questionnaires. Questions were related to mobile adoption, preferences for logistical integration of mobile interventions for continuing care, process variables, and preferences for which of two messages (gain framed vs. loss framed) they would rather receive. They were also asked to write messages they thought would be most helpful to receive during different periods of the change process. Then, as part of the larger treatment development study and not included in this manuscript, they participated in a structured focus group where they were presented with different types of experimenter generated messages and asked to discuss with the group which messages they thought would be most helpful and which ones would be least helpful to receive.
Assessment
Participants completed three separate questionnaires. The first questionnaire was specifically designed to inquire about the acceptability and feasibility of an interactive SMS system for addiction care. Domains covered in this questionnaire included demographics, cell phone usage and features, acceptability, and helpfulness of SMS for addiction care, and preferences for specific types of features within a system. The second questionnaire consisted of items intended to assess for specific participant characteristics covering a wide range of domains (e.g., demographics, treatment history, harm appraisals) that could guide text messages. These items were taken from a questionnaire specifically designed for a web-based assessment and adapted for the current study. For the current study, we focused only on the single-item process variables including overall harm from substance use and benefits to being drug free, derived from the Primary Appraisal of Harm Measure (PAM: Morgenstern, Labouvie, McCrady, Kahler, & Frey, 1997), self-efficacy and commitment/readiness derived from readiness rulers (Heather, Smailes, & Cassidy, 2008; Sobell & Sobell, 1995). The response format for each item was a 7-point Likert-type scale ranging from not at all (1) to extremely (7).
The third questionnaire focused on participants' preferences for certain types of messages. This questionnaire was comprised of two domains. The first domain consisted of six experimenter developed messages that were nearly identical in their wording, except for modifications designed to either highlight the benefits of changing or the consequences of not changing. We then asked participants to choose the message they preferred of the two similarly worded items. In the second domain, participants were asked to create messages (i.e., 160 characters or less) that they felt would be most helpful at four different time periods of recovery, including treatment entry, after 90 days being drug free, when an individual was at risk for relapse, and if an individual had relapsed.
In an attempt to classify the messages written by participants for each researcher-defined time period (e.g., at risk for relapse), we used the taxonomy of behavior change techniques developed by Abraham and Michie (2008) and the processes of change developed by Prochaska and DiClemente (1984; Prochaska, DiClemente, & Norcross, 1992). Because several of the techniques and processes, such as prompt self-monitoring of behavior, are inherent in text messaging (i.e., getting a relevant text message itself is a prompt, regardless of the content of the text) or were not relevant (e.g., model the behavior or agree on behavioral contract, both of which are described as physical actions), we only used a subset of strategies primarily taken from the processes of change. Messages written by participants for each of four situations (treatment entry, 90 days being drug free, at risk for relapse, and actual relapse) were coded into 10 behavior change techniques including messages that fostered helping relationships, self and environmental reevaluation, motivation/self-liberation, spirituality, general encouragement/self-efficacy, cognitive reframing, behavioral counterconditioning, consciousness raising, reinforcement management, and stimulus control. Helping relationships included anything that fostered social support, whether from a group (e.g., get to a meeting) or an individual (e.g., call a friend now). During the coding process, we added another category called Alcoholics Anonymous (AA) “pearl” due to the number of individuals writing messages using slogans such as “one day at a time” or “easy does it.” Although these could be seen as a cognitive reframe, we decided to keep them as their own category to differentiate between these and other types of cognitive reframing.
Statistical Analyses
A dichotomous variable was created regarding a preference for messages concerning the benefits versus the consequences of treatment, based on a participant's majority preference (i.e., at least 2/3 of benefit or consequence messages were selected). This was used to run univariate analysis of variance (ANOVA) among the five process variables, and all significant relationships were regressed on the dichotomous preference variable using logistic regression. For user generated messages, two researchers independently coded all text messages using the taxonomy described above. Each was given a definition and example of a message for each category. Agreement was dichotomous (yes/no). In cases where there was disagreement about the categorization of a message, the message was discussed and subsequently placed either in a category, based on agreement between both coders or marked as uncoded and excluded from the analyses. Of the 317 messages provided by the participants, 8 (2.5%) messages were left uncoded and dropped due to either rater disagreement about the proper classification or because they did not make sense in the context of the assignment. The remaining 309 messages were coded by two reviewers, resulting in 89% agreement. Figure 1 presents the percentage of messages corresponding to each type of behavior change strategy by situation.
Figure 1. Preferred behavior change strategy based on stage of treatment.
Results Participant Characteristics and Feasibility of Text Messaging System
The majority of participants owned at least one phone in the past year (n = 45, 90.0%), with an average of 2.9 phones (SD = 2.4; Range: 0–15) in the past 3 years. About half of the participants had additional features on their phones, most notably, 62% (n = 31) could send an attachment in a text, 56% (n = 28) had access to the Internet, 46% (n = 23) could download applications, 48% (n = 24) had a camera, 40% (n = 20) had audio recording, and 32% (n = 16) had video recording and playback. Roughly half of those who knew of their messaging plan had unlimited text messages (n = 22, 48.0%). Some individuals received an overwhelming number of text messages per week (e.g., 900). Therefore the top 5% of responses were excluded to provide a more representative mean. Excluding the top 5%, participants received a mean of 17.3 (SD = 27.5) texts in the past week.
Interest in Using the System
Almost all participants (n = 49, 98%) reported they would be interested in using an interactive text messaging system during and after treatment. Thirty-four percent (n = 17) of individuals thought the system would be most helpful at treatment entry, 22% (n = 11) during later stages of treatment and 44% (n = 22) following treatment. Almost two thirds (62%) reported they would prefer receiving at least one message daily, as compared with 14% who reported preferring weekly messages. When asked about whether they would use certain system features, 80% (n = 40) reported they would inform the system of a lapse to drug use, 84% (n = 42) would send a “help message” if they were in a high-risk situation for using drugs and looking for guidance, 78% (n = 39) would want their counselor alerted if at risk for relapse, and 96% (n = 48) would want a friend alerted in this situation. Sixty percent (n = 30) of individuals reported no concerns with the use of addictions terms (e.g., using, drugs) in SMS messages.
Message Preferences
Table 1 presents the results of the user messaging preferences comparing consequence driven versus benefit driven messages. Individuals tended to prefer messages about the benefits of changing (64%) versus the consequences of not changing (34%). However, as noted in Table 1, one message about imagining the benefits of changing versus the consequences of not changing was evenly endorsed. To understand whether process variables predicted message preferences, we compared scores on the four process rulers (readiness for change, benefits of changing, harm of past use, and confidence to change) for those who preferred benefit driven messages to those who preferred consequence driven messages using univariate ANOVA. Individuals who preferred benefit driven messages had significantly higher ratings for perceived benefits to being drug free, F(1, 45) = 8.76, p < .05, and readiness for change, F(1, 45) = 4.84, p < .05. There were no significant differences between groups on ratings of self-efficacy or harm from past use. Step-wise logistic regression revealed that the level of benefit from being drug free mediated the relationship between readiness for change and choosing benefit driven messages accounting for 12.5% of the variance, R2 = .125, incremental F(2, 44) = 4.28, p < .05.
Preference for Benefit Versus Consequence Focused Messages
Only those strategies that had approximately a 5% endorsement for at least one situation were included in Figure 1. Those excluded were stimulus control and reinforcement management. Overall, helping relationships was most frequently endorsed at all time points with significant increases during risk for a lapse (30%) and following a lapse (53%). Motivational messages (self-liberation) followed an opposite trend, with higher values early in the change process and at the 90-day mark. As could be expected, general encouragement/efficacy messages spiked at the 90-day mark, reinforcing success; reevaluation messages were written early in the change process and during risk for a lapse. The AA pearls accounted for about 15% of messages for all periods aside from following a lapse (6.1%). Spirituality messages remained fairly constant across situations at just under 5%.
DiscussionThe current exploratory study revealed that individuals in substance abuse treatment would be interested in receiving text messages that support their recovery and that the messages individuals believed would be most helpful to them differed depending on the point in the change process. Although participants reported that benefit driven messages were perceived as more helpful than consequence driven messages, these preferences differed based on their ratings of their personal perceived benefits for change and readiness for change, suggesting that message tailoring to preferences and readiness for change can be a useful undertaking, a finding consistent with the computer-based intervention literature.
The use of SMS in continued care appears feasible, as 90% of even the most disenfranchised individuals in substance abuse treatment had mobile phones and expressed willingness to use SMS as a continuing care intervention. Although nearly all phones are SMS capable, making it a widely disseminated and far-reaching medium for intervention, it is striking that nearly half of the sample had smart phones, an adoption rate that is higher than the general U.S. adoption rate at the time of the study (Neilsen, 2011). This highlights the possibility of expanding mobile web features, tracking applications, and adding audio and video messaging to mobile intervention development with disenfranchised populations. It was also notable that 78% of individuals wanted a counselor alerted and 96% would want a friend alerted if they were at risk for relapse, validating current trends in increasing social networking and support through technology as an important component of behavior change interventions. Moreover, there was a high level of endorsement for using help and crisis messaging, a component that would be proactively initiated by the user. These results emphasize a need and role for both proactive and reactive pushing of intervention content.
It is worth mentioning that participants' desires may differ from clinicians' recommendations. In a separate survey (Muench & Weiss, 2011), we examined the preferences of 34 addiction treatment providers and found that though 87% would use a similar system as part of their care, providers were less likely to prefer the instant alert option, with only 8% wanting to be alerted to a possible relapse in the moment. However, 80% would be interested in some type of alert, with 31% opting for an e-mail, and 40% desiring an alert the next working day. Interestingly, this former survey also revealed that only 11% of providers believed the system would be most beneficial after treatment, as compared with 44% of clients, stressing the importance of taking both provider and client perspectives into account when developing systems that extend the reach of care.
One concern that may inhibit effectiveness was that individuals had an average of 2.9 cell phones in the last year. A recent study revealed that service interruptions and length of time with the same phone number was the best predictor of outcome in an SMS intervention study of oral contraceptive use in a low-income inner-city sample of women (Castaño, Bynum, Andrés, Lara, & Westhoff, 2012). These potential obstacles to continued and consistent phone service suggest the need for proactive techniques to improve engagement in messaging and other phone related mobile interventions, such as collateral phone numbers when client phones may be inactive. That only 60% of participants approved of using language that could potentially identify them as an individual in recovery suggests that mobile interventions need to take the privacy needs of individuals into account and that more research is needed in developing acceptable mobile interventions for those attempting to change addictive behaviors. For example, in our mobile intervention development study for problem drinkers, we are developing “mirrored” messages in which participants can choose a code word (e.g., coffee, soda) to replace the word alcohol in messages for confidentiality purposes. Overall, despite some barriers to effective integration, the user characteristics and preferences found in the current study indicate that mobile interventions for the addictions are very promising.
General preferences for benefit-driven messages correspond to the general message tailoring literature. For example, a metaanalytic review of disease prevention messages found that gain-framed appeals, which emphasize the advantages of compliance with the communicator's recommendation, are slightly more persuasive than loss-framed appeals, which emphasize the disadvantages of noncompliance (O'Keefe & Jensen, 2006). In addition, individuals were drug free for over 3 months, which corresponds to the theory that benefit driven interventions may be more efficacious for individuals in maintenance stages (Rothman et al., 2004). However, higher scores on the benefits of being drug free were the uniquely associated with preferring benefit driven messages, emphasizing the need for tailored messaging programs based on decision support paradigms. These findings correspond to the health promotion literature on messaging that is congruent with motivational styles being more efficacious than incongruent messaging (e.g., Mann, Sherman, & Updegraff, 2004) and therefore suggests that there is a base from which to draw tailoring content for messaging interventions.
User generated content that related to researcher defined points during the treatment and recovery process revealed interesting information as to the strategies individuals in drug treatment think they may use. There were some logical patterns in the data, such as the spikes in messages that fostered social support during times of crisis; messages that encouraged individuals to change early in the change process; messages that trigger self and other reevaluation during transition or decision points (e.g., early in treatment and at risk for relapse); and messages that encourage self-efficacy when milestones are met (e.g., 90 days clean). We were surprised to find that some techniques associated with positive outcomes in the addiction treatment process literature, such as avoiding high-risk situations, were not written about more often across periods of recovery.
As noted earlier, each contact in a mobile intervention should be considered a stand-alone intervention that is a part of a larger whole. Messages should be designed to intervene with the user in their environment, in response to the particular time and place both physically and in their recovery using ecological momentary interventions. Self-regulation theory highlights that very different techniques may be more effective or used more depending on the level of arousal or at different time points (e.g., cognitive reappraisals for lower arousal and distraction for higher arousal; Sheppes & Gross, 2010). Results of the current study stress that individuals know that reappraisal or reevaluation and helping relationships are important when one might have high craving, but that once a lapse has taken place, social support is the crucial factor. Including user driven content has numerous advantages but has not been utilized often in the treatment development literature. Coding these messages based on empirically derived behavior change techniques can help determine the active ingredients and preferences of the individuals receiving these mobile interventions.
Overall, results highlight the promise of using mobile interventions for addiction care and underscore the need for empirically tested models for this new medium. Mobile applications for addictions are readily available in smart phone app stores, and whereas some are based on empirically sound intervention strategies, very few are using the available theoretical underpinnings of effective treatments (Cohn, Hunter–Reel, Hagman, & Mitchell, 2011). The mobile phone can enhance many techniques such as: relapse prevention, tailored individualized messaging, self-regulation in the moment, increasing salience of therapeutic cues and social support alerts—all of which have been theorized or known to be effective mechanisms of change. Moreover, the mobile phone allows for new theoretically based interventions which have been explored less in the addictions field, such as self-modeling interventions through user generated content (e.g., record me when I am motivated and play it back later; Muench, Tryon, Travaglini, & Morgenstern, 2006). Finally, more research is needed to understand both how user preferences will drive intervention development, as well as which types of messages are effective for which types of individuals.
This exploratory study had several limitations and results should be viewed as preliminary until further research is conducted. The intent of this study was to begin to establish a foundation of knowledge about the feasibility and acceptability of an SMS system for addiction continuing care and to begin to develop the content for a pilot intervention using such a system. Its aim was not to pilot test the efficacy of the system. The primary limitation is that this study did not examine the preferences of these messages in a real world setting where individuals could rate the benefits to measure preferences in certain situations. Future research should include sending the types of messages individuals find helpful in vivo at various points during treatment, and/or to test different types of messages (e.g., self-written vs. gain framed) and compare outcomes. Although this study only examined preferences, it is still useful to understand what individuals in treatment prefer. Nonetheless, there may be a discrepancy between what is objectively most effective for patients and what is perceived as most effective by patients. However, even if this discrepancy exists, it is likely that once a patient returns to his or her home environment (or is allowed to leave a legally required treatment program), he or she will continue to engage in strategies perceived as effective. A second limitation is that our sample skewed male, and our population was a disenfranchised inner-city treatment population potentially limiting the generalizability of these findings. Our sample was too small to examine gender differences or race/ethnicity differences, but such characteristics could have a profound impact on which messages are preferred by users. Future studies can improve on these limitations and test the real world efficacy of mobile interventions with substance abusing populations.
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Submitted: December 19, 2011 Revised: July 20, 2012 Accepted: August 13, 2012
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 315-321)
Accession Number: 2012-24205-001
Digital Object Identifier: 10.1037/a0029963
Record: 21- Development and evaluation of the Marijuana Reduction Strategies Self-Efficacy Scale. Davis, Alan K.; Osborn, Lawrence A.; Leith, Jaclyn; Rosenberg, Harold; Ashrafioun, Lisham; Hawley, Anna; Bannon, Erin E.; Jesse, Samantha; Kraus, Shane; Kryszak, Elizabeth; Cross, Nicole; Carhart, Victoria; Baik, Kyoung-deok; Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014 pp. 575-579. Publisher: American Psychological Association; [Journal Article] Abstract: To evaluate several psychometric properties of a questionnaire designed to assess college students’ self-efficacy to employ 21 cognitive–behavioral strategies intended to reduce the amount and/or frequency with which they consume marijuana, we recruited 273 marijuana-using students to rate their confidence that they could employ each of the strategies. Examination of frequency counts for each item, principal components analysis, internal consistency reliability, and mean interitem correlation supported retaining all 21 items in a single scale. In support of criterion validity, marijuana use-reduction self-efficacy scores were significantly positively correlated with cross-situational confidence to abstain from marijuana, and significantly negatively correlated with quantity and frequency of marijuana use and marijuana-related problems. In addition, compared with respondents whose use of marijuana either increased or remained stable, self-efficacy was significantly higher among those who had decreased their use of marijuana over the past year. This relatively short and easily administered questionnaire could be used to identify college students who have low self-efficacy to employ specific marijuana reduction strategies and as an outcome measure to evaluate educational and skill-training interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Development and Evaluation of the Marijuana Reduction Strategies Self-Efficacy Scale / BRIEF REPORT
By: Alan K. Davis
Department of Psychology, Bowling Green State University;
Lawrence A. Osborn
Department of Psychology, Bowling Green State University
Jaclyn Leith
Department of Psychology, Bowling Green State University
Harold Rosenberg
Department of Psychology, Bowling Green State University
Lisham Ashrafioun
Department of Psychology, Bowling Green State University
Anna Hawley
Department of Psychology, Bowling Green State University
Erin E. Bannon
Department of Psychology, Bowling Green State University
Samantha Jesse
Department of Psychology, Bowling Green State University
Shane Kraus
Department of Psychology, Bowling Green State University
Elizabeth Kryszak
Department of Psychology, Bowling Green State University
Nicole Cross
Department of Psychology, Bowling Green State University
Victoria Carhart
Department of Psychology, Bowling Green State University
Kyoung-deok Baik
Department of Psychology, Bowling Green State University
Acknowledgement:
Marijuana is the most commonly used illegal drug in the United States, including among university students (Johnston, O’Malley, Bachman, & Schulenberg, 2012). Although many students use marijuana with few, if any, harmful consequences, excessive use of marijuana is associated with a variety of health, social, and academic problems (Bell, Wechsler, & Johnston, 1997; Caldeira, Arria, O’Grady, Vincent, & Wish, 2008; Hammersley & Leon, 2006; Lee, Neighbors, Kilmer, & Larimer, 2010; Shillington & Clapp, 2001). The health and welfare of university students who use marijuana regularly could be maintained or improved if they employed cognitive–behavioral self-control strategies to reduce the amount and/or the frequency of consumption (e.g., Roffman & Stephens, 2012; Rooke, Copeland, Norberg, Hines, & McCambridge, 2013).
Despite the advantages and potential effectiveness of employing cognitive–behavioral self-control strategies to reduce marijuana use, there is little research examining the extent to which these strategies are employed and the barriers that impede their implementation. One factor that may influence the implementation of reduction strategies is self-efficacy (Bandura, 1977). Although investigators have developed abstinence self-efficacy questionnaires to evaluate drug takers’ confidence to refrain from using a target drug in various contexts (Annis & Martin, 1985; Sklar, Annis, & Turner, 1997), we could find no instrument designed to assess users’ self-efficacy to employ specific strategies intended to reduce their use of marijuana.
Therefore, we designed the present study to develop and evaluate several key psychometric properties of a self-administered questionnaire to measure marijuana users’ self-efficacy to employ various self-control skills to reduce how much and how often they consume marijuana as an alternative or complement to measuring only their confidence to abstain. We recruited a sample of university students who regularly consumed marijuana to assess factor structure, internal consistency reliability, unidimensionality, and criterion validity of the measure. Specifically, although we view confidence to employ use-reduction strategies and confidence to refrain from using marijuana across different circumstances as different types of self-efficacy, we hypothesized that use-reduction self-efficacy would be significantly but only moderately correlated with self-efficacy to refrain from marijuana use. In addition, we expected that lower self-efficacy to employ use-reduction strategies would be significantly correlated with more frequent consumption of marijuana and experience of more marijuana use-related problems. As another evaluation of criterion validity, we tested the assumption that those who had decreased their marijuana use over the past year would report higher self-efficacy to employ use-reduction strategies compared with those whose consumption had remained stable or increased.
Method Procedure and Respondents
Following approval of the project by the institutional review board in the spring of 2012, we sent a recruitment e-mail (with a 1-week follow-up reminder e-mail) to a random sample of 8,000 students, which was approximately 50% of the undergraduates enrolled in the Midwestern public university from which respondents were recruited. The e-mail overture provided a short description of the study, including the eligibility requirements (i.e., at least 18 years of age; used marijuana at least once per month in each of the last 6 months), compensation (i.e., opportunity to win one of two $50 gift certificates from an online retailer), and a web link to the study materials, which were hosted by a commercial survey designer (www.surveygizmo.com). To help ensure anonymity, the survey (which took approximately 30 min to complete) was programmed to automatically provide a gift certificate to the two randomly selected winners immediately after they completed the questionnaires.
Of the 479 individuals who clicked the link to the study materials, 326 submitted survey responses and 300 of these met the eligibility requirements. Of the 300 eligible respondents, 273 were retained because they completed every item on the measure of strategy-specific self-efficacy (in anticipation of list-wise deletion on several SPSS analyses). Evaluation of demographic characteristics of the sample and the larger student body from which they were recruited revealed that 84% of the sample identified themselves as Caucasian, as did 77% of the study body; 93% of the sample were between the ages of 18 and 24, as were 90% of the study body; 45% of our sample were female, as were 55% of the study body; and 36% of the sample lived on campus, as did 38% of the student body. In addition, the sample reflected the full range of years at university (first year = 18%, second year = 26%, third year = 26%, fourth year = 20%, fifth year or higher = 10%). Table 1 provides detailed information on marijuana and other drug use history of the sample.
Marijuana Use and Other Drug Use Characteristics
Measures
Marijuana Reduction Strategy Self-Efficacy Scale (MJ-RS-SES)
To develop the MJ-RS-SES, we (a) created a pool of cognitive–behavioral strategies that an individual might employ to reduce his or her consumption of marijuana and (b) modified items on questionnaires designed to assess past use of (Martens, Pederson, Labrie, Ferrier, & Cimini, 2007) and current self-efficacy to employ (Bonar et al., 2011; Kraus et al., 2012) alcohol reduction strategies. After deleting redundant items and rephrasing potentially ambiguous items, we compiled a list of 21 strategies that comprised the working draft of the MJ-RS-SES (see Table 2 for the list of items). The instructions at the top of the questionnaire asked the respondent to rate his or her current confidence, on an 11-point scale from 0% (not at all confident) to 100% (completely confident) in increments of 10, that he or she COULD use each of the listed strategies to reduce his or her marijuana consumption. Readability statistics indicated that the questionnaire items are easily readable (Flesch-Kincaid Grade Level = 2.6; Flesch Reading Ease = 86.3 on a scale of 0 [most difficult] to 100 [easiest]).
Items, Component Loadings, and Item Means and SDs on the Marijuana-Reduction Strategies-Self-Efficacy Scale (MJ-RS-SES; N = 273)
Marijuana Refusal Self-Efficacy Questionnaire (MRSEQ)
This questionnaire was based in part on a previously published measure assessing refusal self-efficacy for alcohol (Oei, Hasking, & Young, 2005; Young, Oei, & Crook, 1991). Specifically, we modified the instructions to ask about respondents’ perceived self-efficacy to abstain from using marijuana in each of 13 social, emotional, and environmental situations in which marijuana could be consumed. Respondents were asked to rate how confident they were that they could refuse marijuana in each of the listed situations on an 11-point scale from 0% (no confidence, cannot refuse) to 100% (extreme confidence, certain can refuse) in increments of 10. Internal consistency reliability in the current sample was .87.
Rutgers Marijuana Problem Index (RMPI)
This questionnaire (Simons, Correia, Carey, & Borsari, 1998) asks respondents to rate the frequency with which they have experienced each of 17 specific marijuana-related problems over the last year using a 5-point scale ranging from 0 (never) to 4 (10 or more times). Research has supported the internal consistency reliability, predictive validity, and criterion validity of this measure (Lee, Neighbors, Hendershot & Grossbard, 2009; Simons et al., 1998). Internal consistency reliability in the current sample was .85.
Marijuana Use History
We designed this questionnaire to assess the frequency, durThis questionnaire was designeation, and stability of marijuana use, previous attempts to reduce and quit using marijuana, typical location of consumption, typical means of consumption, forms of marijuana consumed, and current experience of intoxication.
Drug Use History
We designed this questionnaire to assess respondents’ use of drugs other than marijuana. Respondents were asked to indicate (i.e., yes or no) whether they had ever used any of the listed substances (i.e., cocaine, heroin, hallucinogens, ecstasy/MDMA, amphetamines, prescription opiates, tranquilizers, sedatives, inhalants, and Spice/K2) and whether they had used these substances in the past 3 months.
Demographics
We designed this questionnaire to assess basic demographic data including age, gender, ethnicity, year in college, and residence on or off campus.
Results Item Reduction
As the initial step in the evaluation of the MJ-RS-SES, we examined the response frequencies for each of the 21 strategies to identify any “unbalanced” frequencies—that is, 75% or more of respondents endorsed that they had either no or very little confidence (0% or 10% ratings) or had very high confidence (90% or 100% ratings) that they could use that strategy. None of the items on the MJ-RS-SES were unbalanced. Next, we examined the item-total correlations to identify items for potential elimination. No items met Ferketich’s (1991) criterion (r < .30) for elimination (item-total rs ranged from .51 to .82).
Principal Components, Unidimensionality, and Internal Consistency Reliability
Next, we conducted a principal components analysis using data from the 273 respondents who rated their confidence on every one of the 21 items of the MJ-RS-SES. The solution was not rotated because we had no a priori basis for assuming the analysis would yield multiple components. This analysis yielded three components with eigenvalues greater than 1.0; however, the scree plot showed obvious flattening after the first component (eigenvalue = 11.3; 53% of variance accounted for) with relatively small eigenvalues (1.5 and 1.1) and proportions of variance (7% and 5%) accounted for by the subsequent two components. In addition, examination of the component loadings (Table 2) revealed that all 21 items had their highest loading on the first component (loadings ranged from .49 to .82), with few items cross-loading on components 2 and 3.
Next, we calculated the mean interitem correlation to evaluate the “unidimensionality” of the 21-item scale. Based on Clark and Watson (1995), we interpreted the resulting coefficient (mean r = .50, interitem rs ranged from .17 to .85) as support for the unidimensionality of the scale. In addition, internal consistency reliability was notably high across the 21 items (α = .96), perhaps in part because of the large number of items on the scale. We interpreted these findings as indicating that the 21 items on the MJ-RS-SES comprise a single scale.
Base Rates of Self-Efficacy to Employ Specific Marijuana Reduction Strategies
As examination of Table 2 reveals, 20 of the 21 items had means indicating that, on average, respondents reported being moderately confident that they could employ these strategies. However, the relatively large standard deviations also indicate that confidence varied considerably with some students having relatively low and others having relatively high self-efficacy to employ each of these 20 strategies. Furthermore, even that one item (“Dilute marijuana you are about to use with tobacco”) with a notably low mean rating (M = 27.7) had a large standard deviation (SD = 36.7).
Evaluation of Criterion Validity
First, we assessed the association of self-efficacy to employ specific marijuana reduction strategies with self-efficacy to abstain from marijuana across various circumstances. As expected, MJ-RS-SES scores were positively correlated with MRSEQ scores, r(271) = .62, p < .01. Second, we assessed the association of reduction strategy self-efficacy with the experience of marijuana-related problems and with quantity and frequency of marijuana consumption. As expected, MJ-RS-SES scores were significantly, albeit less strongly, negatively correlated with marijuana problems, r(271) = −.28, typical number of joints smoked per week, r(271) = −.37, and typical number of days of marijuana use per month, r(266) = −.32, (all ps < .01). Third, an analysis of variance (ANOVA) revealed that mean MJ-RS-SES scores varied as a function of stability of marijuana use over the past 12 months, F(2, 269) = 3.62, p < .05, ηp2 = .03. Specifically, those 82 individuals who reported a decrease in their marijuana use over the past year reported higher self-efficacy (M = 66.8, SD = 22.2) to employ reduction strategies than those 145 individuals who reported that their marijuana use had stayed the same (M = 57.7, SD = 27.5) and those 45 who reported an increase in their marijuana use over the past 12 months (M = 56.9, SD = 27.6).
Association of MJ-RS-SES With Current Intoxication
Although it was not a test of validity per se, we conducted an independent samples t-test to examine whether there were mean differences in self-efficacy as a function of endorsement of intoxication (i.e., yes or no) while completing the survey. Those 43 respondents who endorsed that they were intoxicated while completing the study materials had significantly lower self-efficacy (M = 52.0, SD = 30.8) that they could employ the use-reduction strategies, t(269) = −2.3, p < .05, d = .28, compared with those 228 who stated they were not intoxicated while completing the study materials (M = 61.9, SD = 25.2).
DiscussionIn the present study, we recruited 273 undergraduates who were regular users of marijuana to complete a web-administered measure of their self-efficacy to employ 21 marijuana use-reduction strategies. Based on the distribution of confidence ratings, interitem correlations, principal components analysis, and internal consistency reliability, we decided not to delete any strategies (which helps increase content validity of the questionnaire) and concluded that all 21 items comprise a single scale. Furthermore, the readability statistics indicate that the questionnaire should be easily understood both by university students and by younger and less educated respondents.
This initial evaluation of the MJ-RS-SES supported several aspects of criterion validity; specifically, reduction strategy self-efficacy scores were significantly positively correlated with self-efficacy to refrain from use and significantly negatively associated with quantity and frequency of marijuana use, marijuana-related problems, and having increased one’s use of marijuana over the past year. We also found that self-efficacy was significantly lower among those who reported being intoxicated compared with those who were not. Whether those who were intoxicated have lower self-efficacy because intoxication during participation is a proxy for an unwillingness or inability to employ self-control skills, or whether this difference is an outcome of answering the items while intoxicated, awaits further research using a within-subjects design in which students report their confidence to use these strategies while and while not intoxicated.
We note that the MJ-RS-SES and our study of its psychometric properties have several limitations. For one, not all of the 21 strategies will apply in the many different contexts in which students use marijuana (e.g., using when alone vs. with others; using with friends vs. with strangers; using highly potent vs. less potent marijuana). Furthermore, we did not include an open-ended question asking respondents if they used any other strategies, and we recognize some may employ personally unique reduction strategies not currently listed on the MJ-RS-SES. Another limitation is that respondents’ ratings of their self-efficacy may be inaccurate depending on how insightful and truthful they are regarding their confidence to employ these self-control strategies.
We also note that outlining the inclusion criteria and incentive in the e-mail invitation (considered part of informed consent by our university institutional review board), could have encouraged respondents to misrepresent their marijuana use in order to meet eligibility requirements to qualify to win one of the gift cards. Moreover, we created the MRSEQ because no such questionnaire existed for marijuana and we wanted to ask respondents to rate their confidence to abstain in specific marijuana-related contexts. In addition, we developed our own marijuana use history questionnaire because we wanted to assess additional information—such as respondents’ stability of use, previous attempts to reduce and quit using marijuana, typical means and location of consumption, and current experience of intoxication—that are not typically included on such measures.
Another potential limitation of the study was the relatively low response rate given the total number of e-mail overtures sent. Given recently published nationwide data (Johnston et al., 2012) showing that the prevalence of past-month marijuana use among college students was 19.4%, and our requirement that respondents had to use marijuana at least once per month for the past 6 months, our potential respondent pool was unlikely to be larger than 1,552 monthly marijuana users out of 8,000 university students to whom the e-mail overture was sent. In addition, the time demands of the survey, the limited number of incentives offered, and our having sent the recruitment email only twice, could have decreased the number of potential respondents who decided to participate. Although our sample of marijuana users was notably similar to the campus at large in terms of age, ethnicity, and gender, it may not be fully representative of the population of those who use marijuana.
A combination of intrapersonal, peer, societal, and legal influences may result in more students electing to seek assistance to reduce their use of marijuana as an alternative to either abstinence or excessive consumption. Therefore, despite the limitations outlined above, a self-report questionnaire of self-confidence to employ a variety of marijuana use-reduction strategies has several possible applications. For example, the questionnaire could be used as an outcome measure to assess the degree to which education and prevention interventions impact reported confidence to employ specific reduction strategies, perhaps especially among students who are more likely to experience negative marijuana-related consequences. In addition, for students who are naïve about specific self-control strategies, rating one’s confidence to employ each strategy listed on the MJ-RS-SES might itself serve as a brief intervention that informs young people of specific strategies they could employ to reduce their consumption of marijuana. This last application warrants its own evaluation using a sample that has little knowledge of these skills.
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Submitted: April 14, 2013 Revised: January 29, 2014 Accepted: February 24, 2014
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 575-579)
Accession Number: 2014-24742-015
Digital Object Identifier: 10.1037/a0036665
Record: 22- Discrepancy in caregiving expectations predicts problematic alcohol use among caregivers of trauma injury patients six months after ICU admission. Kearns, Nathan T.; Blumenthal, Heidemarie; Rainey, Evan E.; Bennett, Monica M.; Powers, Mark B.; Foreman, Michael L.; Warren, Ann Marie; Psychology of Addictive Behaviors, Vol 31(4), Jun, 2017 pp. 497-505. Publisher: American Psychological Association; [Journal Article] Abstract: This prospective study examined the influence of caregiving variables on the development of problematic alcohol use among family members of patients admitted to an urban Level I trauma center. Data were collected from 124 caregivers 48 hrs after initial hospitalization of their family member. The final sample included 81 participants (24.6% male; Mage = 47.8) who completed their follow-up assessment at 6 months. Hierarchical linear and logistic regression analyses assessed increases in consumption and odds of a positive screen for problematic alcohol use in association with caregiver burden, actual time spent in the caregiving role, and caregiving differential (i.e., anticipated time spent caregiving at baseline in relation to actual time caregiving at 6 months). At 6 months, 24.7% of caregivers screened positive for problematic alcohol use. Results uniquely highlighted caregiving differential as a significant predictor of both increases in general alcohol consumption (ΔR2 = .06, p < .01) and odds of screening positive for problematic alcohol use at 6 months (Odds Ratio = 1.05, 95% CI [1.02–1.09]). More specifically, our adjusted model found that providing 10% more time caregiving, relative to expectations at baseline, was associated with an increase in the probability of problematic alcohol use by 22% (95% CI: 8–37%) at 6 months. These results suggest that a discrepancy in expectations regarding anticipated time caregiving and actual time caregiving, rather than solely the amount of caregiving or perceived caregiver burden, may be an important predictor of caregiver alcohol use 6 months after a family member’s ICU hospitalization. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Discrepancy in Caregiving Expectations Predicts Problematic Alcohol Use Among Caregivers of Trauma Injury Patients Six Months After ICU Admission
By: Nathan T. Kearns
Department of Psychology, University of North Texas;
Heidemarie Blumenthal
Department of Psychology, University of North Texas
Evan E. Rainey
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center, Dallas, Texas
Monica M. Bennett
Office of Chief Quality Officer, Baylor University Medical Center
Mark B. Powers
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center
Michael L. Foreman
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center
Ann Marie Warren
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center
Acknowledgement: This research was supported in part by the Ginger Murchison Traumatic Brain Injury Fund of the Baylor Health Care System Foundation.
The data presented in this article were previously presented as an abstract at the Society of Critical Care Medication (SCCM) 45th Critical Care Congress in Orlando, Florida, in 2016. This article has not been previously published and has not been submitted elsewhere for publication.
Individuals hospitalized in the intensive care unit (ICU) may be particularly susceptible to issues with alcohol use both before admission and during recovery. Studies of Level I and II trauma centers have reported upward of 52% of patients have positive blood alcohol screens at the time of hospitalization (Cornwell et al., 1998) and that upward of 33% of ICU patients screen positive for problematic alcohol use via self-report and interview assessments (see de Wit, Jones, Sessler, Zilberberg, & Weaver, 2010 for review). Moreover, research has demonstrated that patient alcohol use not only adversely affects their own health, but also the health of their caregivers. For example, Kreutzer and colleagues (2009) found that caregivers of patients who used alcohol excessively experienced significantly more emotional distress. While some research has focused on caregiver-patient relationships, the primary focus of ICU-related research has been on the actual patients and improving their psychological and physiological health (Mitchell, Closson, Coulis, Flint, & Gray, 2000; Robinson, 1991). The effects of ICU admission and recovery on patient caregivers after discharge has been less well-studied.
In the United States, an estimated 34 million family caregivers provide upward of 80% of long-term care to patients (Houser & Gibson, 2008). Previous research indicates risk for maladaptive psychological and behavioral outcomes among these caregivers, following admission of their relative, spouse, or loved one to the ICU. For example, studies show that caregivers suffer from disproportionately elevated anxiety, depression, posttraumatic stress, and complicated grief (Azoulay et al., 2005; Davidson, Jones, & Bienvenu, 2012; Pochard et al., 2005; Warren et al., 2016) and frequently endorse “caregiver burden”—characterized by feelings of helplessness, guilt, anger, and alienation from others (Johnson, Chaboyer, Foster, & van der Vooren, 2001).
Complications that arise from these adverse responses to caregiving may stem from the fact that family members often are assumed to be de facto caregivers, without adequate consultation, preparation, or realistic expectations about what will be required of them after their loved one leaves the hospital (Rotondi, Sinkule, Balzer, Harris, & Moldovan, 2007; Wellard & Street, 1999). Worsening the matter is the fact that medical support and care are typically focused on the patient rather than the caregiver (Blom, Gustavsson, & Sundler, 2013), leaving families to adjust to their new roles on their own. Not only can this affect family members’ ability to be adequate caregivers, it also may hinder their own daily functioning and, subsequently, the recovery and health of the patient if the demands of caregiving exceed their initial expectations without the resources to adjust (McAdam, Fontaine, White, Dracup, & Puntillo, 2012; Verhaeghe, Defloor, Van Zuuren, Duijnstee, & Grypdonck, 2005).
Despite evidence of emotional distress and caregiver burden among family members of ICU patients, little empirical work has been conducted on mechanisms that caregivers use to cope. The limited research that has been done focuses almost exclusively on adaptive coping, such as positive appraisal and acquiring social support (Kosciulek, 1994); however, a substantial and growing literature provides support for a self-medication model of maladaptive coping, whereby individuals use alcohol and other substances as a means of regulating and managing psychological distress (Bolton, Robinson, & Sareen, 2009; Khantzian, 2003). Research demonstrates that elevated posttraumatic stress, anxiety, and depression all may be linked to increased alcohol use in the general population (Conner, Pinquart, & Gamble, 2009; Kushner, Abrams, & Borchardt, 2000), although limited work has examined these links among caregivers, specifically. Rospenda, Minich, Milner, and Richman (2010) found that, in a community sample of caregivers, greater social and emotional burden was significantly associated with increased frequency of drinking, frequency of intoxication, and alcohol use problems. Another study found that family members of intensive care patients reported elevated stress at the time of ICU admission, as well as increased use of alcohol during their loved one’s initial week of hospitalization and throughout their stay in the ICU (Halm et al., 1993). Considering the frequency of caregiver burden endorsements, negative emotions (e.g., helplessness), and prevalence of negative psychological symptoms (i.e., depressive, stress), assessment of caregiver alcohol consumption after discharge from the ICU is a natural, yet missing, next step in the literature. To our knowledge, no study has prospectively investigated problematic alcohol use in a heterogeneous sample of caregivers of ICU patients with a diversity of traumatic injuries and critical care needs.
The present study examined the prevalence and predictors of problematic alcohol use among caregivers after patient admission to the trauma and critical care surgical ICU. More specifically, the current study assessed perceived caregiver burden, actual time spent caregiving, and caregiving differential (i.e., how much time a family member anticipated spending in the caregiver role at admission in relation to actual time spent caregiving) at 6 months post-ICU admission. First, it was hypothesized that each of the caregiving dimensions would positively relate to alcohol use at 6 months after adoption of the caregiving role while controlling for alcohol consumption at admission. Second, it was hypothesized that the effects of caregiving would be robust to the inclusion of several additional relevant covariates (e.g., caregiver age, employment status).
Method Participants
The participants in the current analysis constitute a subgroup of a larger ongoing longitudinal project examining mental health among caregivers of patients admitted to the trauma/critical care ICU of an urban Level I trauma center in the southwestern United States. For the purposes of this study, family members are defined according to the Institute for Patient- and Family-Centered Care as “two or more persons who are related in any way—biologically, legally, or emotionally” (Institute for Patient- and Family-Centered Care, 2015).
A total of 124 participants were screened between March2013 and November 2014. All participants who met the following inclusion and exclusion criteria were approached for screening in the ICU and enrolled consecutively. If multiple family members were present, all family members meeting inclusion/exclusion criteria were approached to participate in the study. Inclusion criteria for participation included (a) both the family member and the patient being 18 years of age or older, (b) the patient being in the trauma/critical care ICU service for longer than 48 hrs with an expected survival greater than 96 hrs, and (c) the family member must anticipate spending time with the patient in a caregiver or supportive role (e.g., emotional, social, financial) after the patient was discharged. Exclusion criteria included the inability to understand written or spoken English at the eighth grade level and the inability to provide at least two forms of contact information for follow up. Participants whose family member had expired prior to 6-month follow-up were excluded from analyses, but remained in the study using a bereavement protocol.
For the final sample, 27 participants who did not complete their 6-month follow-up and 16 participants whose patient expired between the baseline assessment and the 6-month follow-up were excluded. The 81 remaining participants (24.6% male; Mage = 47.8, SD = 13.6) who completed both baseline and 6-month follow-up were retained for primary analyses. Notably, the final sample comprised caregivers of 62 patients, with 13 instances of two caregivers completing their assessment on the same patient and 3 instances of three caregivers completing their assessment on the same patient.
Measures
Participant demographic information was obtained through a standard self-report form administered at baseline, which included age, gender, ethnicity, marital status, education level, employment, and income. As necessary, participant demographic information was extracted or confirmed from the hospital’s trauma registry.
Alcohol use
Problematic alcohol use was measured using the Alcohol Use Disorder Identification Test-Consumption (AUDIT-C). The AUDIT-C is a self-report, three-item screen that has been modified from the original 10-item screen (AUDIT; Babor, Higgens-Biddle, Saunders, & Monteiro, 2001; Bohn, Babor, & Kranzler, 1995; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). Using a 5-point scale, the AUDIT-C assesses frequency of drinking [0 (Never) to 4 (4 or more times a week)], typical consumption amount [0 (1 or 2 drinks) to 4 (10 or more)], and frequency of binge drinking [0 (Never) to 4 (Daily or almost daily)], with total scores ranging on a scale of 0–12. In men, a score of 4 or more is considered a positive screen for identifying hazardous drinking or potential alcohol use disorder; in women, a score of 3 or more is considered positive (Bradley et al., 2003; Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998). In the current study, scores were examined both as a continuous total, as well as a sex-specific dichotomous variable (i.e., 0 = negative, 1 = positive screen). The AUDIT-C is a recommended screening tool and has been validated to detect risky drinking, alcohol abuse, and dependence (Bush et al., 1998; Frank et al., 2008; Powers et al., 2014; Volk, Steinbauer, Cantor, & Holzer, 1997).
Caregiving
Four aspects of caregiving were assessed: Caregiver Burden, Anticipated Time Caregiving, Actual Time Caregiving, and Caregiving Differential.
Caregiver burden
The Caregiver’s Burden Scale (CBS) was used to assess perceived caregiver burden during the 6-month follow-up. The CBS is a 22-item scale designed to assess subjectively experienced burden by caregiver’s of chronically disabled persons (Lindvall et al., 2014). The items on the CBS assess five factors: general strain, isolation, disappointment, emotional involvement, and environment. Responses are scored on a 4-point, Likert-type scale ranging from 1 (Not at all) to 4 (Often). Total scores are calculated by averaging all 22 items. The CBS has demonstrated adequate to good psychometric properties (Elmståhl, Malmberg, & Annerstedt, 1996).
Anticipated time caregiving and actual time caregiving
The amount of time caregivers anticipated spending in the caregiver role was measured through a single-item, face valid question asked during the baseline assessment. More specifically, participants were asked, “How much time do you anticipate spending in the caregiving role for your patient?” Responses ranged from 0% to 100%, with 0% indicating they would be spending no time in the caregiver role and 100% indicating they would spend all of their time in the caregiver role.
Similarly, the amount of time caregivers actually spent in the caregiver role was measured through a single-item, face valid questions asked during the 6-month follow-up. Using the same 0–100 response format, participants were asked, “What percentage of your time do you spend in a caregiver role for him/her?,” with 0% indicating that they spent no time in the caregiving role and 100% indicating that they spent all of their time in the caregiving role.
Caregiving differential
Caregiving differential was calculated to measure the difference in the amount of the time the caregiver anticipated spending in the caregiving role versus how much time they actually spent in the caregiving role over the course of 6 months. Caregiving differential was calculated by subtracting Anticipated Time Caregiving (at baseline) from Actual Time Caregiving (at 6 months). Scores ranging from −100 to 100, with −100 indicating absolute overestimation of the time they would spent in the caregiving role (i.e., they expected to spent 100% of their time caregiving, but, over the course of 6 months, they actually spent 0% of their time caregiving) and 100 indicating an absolute underestimation of the time they would spend in the caregiving role (i.e., they expected to spent 0% of their time caregiving, but, over the course of 6 months, spent 100% of their time caregiving).
Procedure
Approval was obtained from the medical center’s Institutional Review Board. Researchers identified potential participants for the study from daily trauma census records, referrals from medical personnel (e.g., ICU nursing staff), and biweekly trauma rounds. Participants who met inclusion/exclusion criteria were approached in the waiting room and at bedside, as appropriate (e.g., participants were not immediately approached if the patient or family members were visibly distressed or if medical personnel were attending to the patient). Participants expressing interest in participating in the study were voluntarily consented and enrolled consecutively. If multiple family members were present in the waiting room or at bedside, all family members meeting inclusion/exclusion criteria were approached to participate in the study. Of note, hospital ICU policy typically limits the number of family members in the ICU rooms to 2–3 people at any given time. Baseline measurements were collected during initial inpatient admission to the trauma/critical care ICU. Six-month follow-ups were collected within a 4-week window around the participants’ “due date” (e.g., two weeks before/after 6 months from the date of the baseline assessment). Reminder postcards or emails based on participants’ preference during admission were sent one week prior to the 4-week window opening. Participants were contacted by trained clinical researchers over the telephone using the contact information provided at baseline, with a maximum number of 12 attempts to successfully contact the participant. The same measures were administered during these 6-month follow-up calls as at baseline. A list of community referrals was provided to all participants at baseline and then again if requested at follow-up.
Data Analysis
Analyses were performed using SAS 9.4 (SAS Institute, 2014). All variables were summarized using means and standard deviations for quantitative variables and counts and percentages for nominal variables (e.g., gender). Independent samples t tests and chi-square tests were used, as appropriate, to compare demographic information for participants who screened positive (i.e., scores ≥4 on the AUDIT-C for males, ≥3 for females) for problematic alcohol use at their 6-month follow-up against those who did not screen positive.
For the analyses that looked for associations between AUDIT-C and caregiving, AUDIT-C was used as both continuous and dichotomous. For the continuous analysis, partial Spearman rank correlations (pr) that controlled for baseline AUDIT-C score were used to evaluate associations between AUDIT-C total scores at 6-month follow up, caregiver burden, time spent caregiving at 6-month follow-up, and caregiving differential. Caregiving variables that were significantly associated with AUDIT-C were then evaluated further with a hierarchical linear regression analysis.
The first step of the model included demographic variables that were significantly different between AUDIT-C groups (i.e., negative vs. positive screen) at the α = .10 level; more specifically, age, gender, education (i.e., college degree vs. no college degree), employment status (i.e., unemployed vs. employed) and baseline AUDIT-C score. Caregiving variables that were significantly correlated with AUDIT-C at 6 months (i.e., caregiving differential) were included in the second step of the model. Change in the coefficient of determination (ΔR2) from the first step of the model (i.e., demographics) to the second next step (i.e., caregiving) was assessed, demonstrating how much variation, above and beyond the covariates, that the caregiving variable explained in predicting AUDIT-C at 6-month follow-up.
The analysis for the binary AUDIT-C outcome (i.e., positive screen vs. negative screen) followed a similar approach. First, all caregiving variables were assessed to determine which were significantly associated with a positive screen using logistic regression models that only controlled for baseline AUDIT-C. Caregiving variables that were significant at this step were then evaluated further. As with the previous analyses, a hierarchical logistic regression model was constructed, including significant demographic variables in the first step and caregiving variables in the second step of the model. For these logistic regressions, area under the receiver operating curve (i.e., c-statistic) were calculated to assess the change in the accuracy of the predictability of a positive AUDIT-C screen from the model with no caregiving to the model that included caregiving.
Results Descriptive Statistics
Descriptive statistics for the final sample are presented in Table 1. As can be seen, the sample was predominantly middle-aged (M = 47.8, SD = 13.6), female (75%), white (42%), married (59%), employed (57%), and held a college degree (42%). Relationship to the patient was relatively diverse, with 19 participants indicating that the patient they would be caring for was their spouse, 22 for their parent, 13 for their child, 7 for their sibling, and 20 for someone other than a legal or blood relative (e.g., close friend, domestic partner). In our sample, 22 of the 81 participants (27.2%) that completed their follow-up assessment screened positive for problematic alcohol use at baseline (via the AUDIT-C), while 20 of the 81 (24.7%) screen positive for problematic alcohol use at 6 months. As noted in Table 1, no significant differences were found between participants who completed their 6-month follow-up and those who did not complete their follow-up on any recorded demographic or pertinent nondemographic variables at last assessment.
Comparison of Baseline Characteristics for Those Who Did and Did Not Have 6-Month Follow-Up Data
Preliminary Analyses
In Table 2, the demographic characteristics of 6-month positive screen participants were compared against those with negative screens for problematic alcohol use. Participants in the problematic alcohol use group at 6 months were significantly more likely to be of younger age (p < .001), have scored significantly higher on the AUDIT-C at baseline (p < .001), and have screened positive for problematic alcohol use at baseline (p < .001). Further, participants who were male, had higher education (i.e., having a college degree vs. no college degree), and were employed (i.e., employed vs. not employed) were significantly more likely to screen positive for problematic alcohol use at the α = .10 level. These variables (i.e., gender, education, and employment status) were subsequently included as covariates in the primary analyses, in addition to age and AUDIT-C baseline scores.
Comparison of Baseline Demographics Between Problematic Alcohol Use Groups at 6-Month Follow-Up and Descriptives of 6-Month Caregiving Variables
For the assessment of problematic alcohol use as a continuous variable, a correlation matrix detailing associations between AUDIT-C scores at 6 months and the three caregiving variables, controlling for baseline AUDIT-C scores, is presented in Table 3. Consistent with previous literature, caregiver burden and actual time spent caregiving were significantly associated (pr = .47, p < .001); moreover, caregiver burden and caregiving differential were significantly correlated (pr = .36, p = .001). Importantly, our results indicated that neither caregiver burden (p = .755) nor actual time spend caregiving at 6 months (p = .360) were significantly correlated with caregiver AUDIT-C scores at 6 months. However, caregiving differential was significantly associated with problematic alcohol use (p = .009), such that trending toward underestimation of time spent in the caregiver role related to higher scores on the AUDIT-C at 6 months.
Partial Spearman Correlations Adjusting for AUDIT-C Total Score at Baseline
For the assessment of problematic alcohol use as a dichotomous variable (i.e., positive vs. negative screen at 6 months), a series of logistic regression models assessing odds of a positive screen for each of the caregiving variables, controlling for baseline AUDIT-C screen, were conducted. Neither caregiver burden (Odds Ratio [OR] = 1.03, 95% CI [.99–1.07], p = .162) nor actual time spent caregiving at 6 months (OR = .45, 95% CI [.13–1.58], p = .212) were significantly associated with screening positive for problematic alcohol use. However, consistent with the preliminary analyses for continuous AUDIT-C scores, caregiving differential did significantly increase odds of a positive AUDIT-C screen (OR = 1.04, 95% CI [1.01–1.08], p = .006).
Primary Analyses
With caregiving differential being the only caregiving variable significantly associated with problematic alcohol use, a hierarchical linear regression analysis was conducted for this variable alone. The first step of the model included significant demographic variables (age, gender, education, employment status) and baseline AUDIT-C as covariates (R2 = .45). Caregiving differential was added into the second level, and did significantly improve the overall model (R2 = .51).
Similarly, with caregiving differential being the only caregiving variable that significantly increased odds of screening positive for problematic alcohol use, a hierarchical logistic regression analysis was conducted for this variable alone. The first step of the model included significant demographic variables and baseline AUDIT-C screen as covariates (c-statistic = .88). Caregiving differential was added into the second level of the model. Results indicated that, above and beyond covariates, caregiving differential significantly predicted increased odds of screening positive for problematic alcohol use (OR = 1.05, 95% CI [1.02–1.09], p = .003, Δc-statistic = .07). More specifically, our adjusted model found that providing 10% more time caregiving, relative to expectations at baseline, was associated with an increase in the probability of problematic alcohol use by 22% (95% CI [8–37%]) at 6 months. Results from these analyses are presented in Table 4.
Results From Analyses of Caregiving Differential Predicting Continuous AUDIT-C Score and Odds of a Positive AUDIT-C Screen
DiscussionAlthough limited work has been conducted to prospectively investigate the negative psychological and physiological effects of ICU patient caregiving on family members, some cross-sectional and short-term prospective research suggests an association between increases in ‘burden’ on the caregiver and caregiver alcohol consumption (e.g., Marsh, Kersel, Havill, & Sleigh, 1998). Drawing on work indicating elevated levels of caregiver burden, increased risk of psychological symptoms (e.g., depressive; Pochard et al., 2005), and problematic drinking by caregivers during patient ICU stay (Halm et al., 1993), this study aimed to advance the literature by assessing associations between three pertinent caregiver variables (i.e., perceived caregiver burden, actual time in the caregiving role, and caregiving differential) and caregiver problematic alcohol use 6 months after initial patient admission. Our results suggest that a discrepancy in expected and actual time caregiving, rather than solely the amount of caregiving or perceived caregiver burden, may be an important predictor of caregiver alcohol use 6 months after a family member’s ICU hospitalization. For instance, family members who spent more time in the caregiver role were no more likely than those who spent relatively less time caregiving to endorse problematic levels of drinking (e.g., a positive screen on the AUDIT-C). However, family members who trended toward underestimating time spent caregiving were significantly more likely to screen for problematic alcohol use than those who trended toward overestimating their caregiving role at 6 months. Together, the findings from this study underscore the importance of assessing alcohol use when evaluating the long-term health of caregivers, and calls attention to caregiver expectancies as a potential point of intervention for medical personnel to reduce risk of problematic drinking among caregivers.
The null findings in regard to both caregiver burden and actual time spent in the caregiver role were somewhat surprising given a) previous research indicating an association between caregiver burden and increased alcohol use (Rospenda et al., 2010) and b) established work demonstrating that caregiver burden increases with the amount of time and resources allocated toward providing for the patient (Bugge, Alexander, & Hagen, 1999; Livingston, Brooks, & Bond, 1985; Winstanley, Simpson, Tate, & Myles, 2006). In regard to the null caregiver burden results, it is possible that over the course of 6 months, individuals may learn to adapt to short-term perceptions of social and emotional burden; thus, greater caregiver burden alone is not enough to evidence a positive relation with problematic use at 6 months. However, elevated caregiver burden in combination with an underestimation of time spent in the caregiving role may interrupt or impede the development of adaptive coping and exacerbate risk for long-term development of drinking problems. Of note, caregiver burden and caregiving differential were significantly correlated. However, the current sample was underpowered to adequately test for these more complex relations; future efforts designed a priori to examine such additive and interactive effects are needed.
Sociopsychology “role theory” may provide one explanation for the null results regarding actual time caregiving (e.g., Biddle, 2013). Extensions of this theory to alcohol research suggest that individuals who take on multiple or additional roles are less likely to drink because of the increased demands of those new roles (e.g., less free time; Hajema & Knibbe, 1998; Wilsnack & Wilsnack, 1991). Although not significant, our results generally supported this theory, with preliminary analyses showing a negative association between actual time caregiving and problematic alcohol use at 6 months. These findings suggest that actual time spent caregiving is, at a minimum, not a significant predictor of alcohol use problems and, alternatively, may actually serve as a protective factor within this population, depending on other contextual variables (e.g., anticipated caregiving). Moreover, while Rospenda and colleagues (2010) did find that individuals who experienced increases in social and emotional burden were at increased risk for alcohol use problems, they found no such association between increases in “time-dependent” burden (i.e., the perceived impact caregiving has on the caregiver’s time) and problematic drinking. Collectively, past empirical work and the current findings suggests that time spent in the caregiver role, independent of other factors, may not be a meaningful predictor of subsequent alcohol use.
The finding that caregiving differential was related to problematic alcohol use at 6 months is consistent with the extensive literature concerning the importance of predictability and control in adaptation to stress (e.g., Affleck, Tennen, Pfeiffer, & Fifield, 1987). This work emphasizes perception of control over stressors as a key contributor to adaptive coping, both in terms of acute response (e.g., psychobiological indices; Koolhaas et al., 2011) as well as maintaining factors and mental health outcomes (Feldner, Monson, & Friedman, 2007). Misevaluation of the amount of time and engagement required reflects a break in the continuity of predictability and control over the family members’ caregiving role, which can be especially disruptive and distressful (Thompson, 1981). A growing literature shows that family members experiencing distress in their caregiving role may neglect their own health and well-being (see Johnson et al., 2001 for review); more specifically, those caregivers may be more likely to avoid adaptive coping or health-promoting activities (e.g., therapy, exercise) and engage in maladaptive strategies, such as coping-related alcohol use (Gallant & Connell, 1997; Northouse, Katapodi, Schafenacker, & Weiss, 2012). This interpretation of the data also parallels work examining self-medication as a form of maladaptive coping, whereby hazardous drinking is initiated or maintained in an effort to manage psychological distress (Khantzian, 1997, 2003; Miller, Vogt, Mozley, Kaloupek, & Keane, 2006; Read, Brown, & Kahler, 2004). It is important to note that the current study did not directly examine the perception of control or coping-related alcohol consumption; future efforts assessing these factors, including motives for drinking generally (e.g., Drinking Motives Questionnaire—Revised; Cooper, 1994) and/or specifically (e.g., subsequent to specified caregiving activities), as well as additional factors that may influence these relations (e.g., social or caregiving support, relevant psychological problems) are needed to better understand and, subsequently, intervene in the development of problematic drinking among caregivers.
Together, the current data suggest that providing caregivers with the information necessary to establish realistic expectations regarding their future caregiver role may reduce their long-term risk of problematic alcohol use. However, research on family members’ satisfaction with communication from providers demonstrates that this information often is not adequately disseminated during their time in the hospital. For example, a review of the literature on the needs and experiences of family members in the ICU found that medical personnel (i.e., nurses, doctors) have a tendency to underestimate the needs of patients’ family members, including their desire for accurate and comprehensive information about daily care of patients (Verhaeghe et al., 2005). In an ethnographic review of family issues in home-based care, Wellard and Street (1999) highlighted the ‘normalization’ of providers (e.g., nurses, doctors) assuming that family members are willing and prepared to take on the caregiver role after their loved one leaves the ICU. Collectively, this work highlights the need for structured education to help caregivers better understand the needs of the patient and pushes for the development of collaborative communication strategies among patients, providers, and caregivers to help establish realistic expectations for treatment requirements beyond the hospital stay. Future longitudinal studies should investigate caregivers’ ‘real-time’ or retrospective assessment of the information that was provided to them by nurses, doctors, and practitioners, and whether this communication adequately prepared them for their caregiving role.
Research also has highlighted a lack of support services available for caregivers, particularly those with diverse cultural or linguistic needs (Whittier, Scharlach, & Dal Santo, 2005; Ham, 1999). Work examining the efficacy of relevant support groups indicates positive effect on caregivers’ psychological well-being, coping effectiveness, and reduction in caregiver burden (Chien et al., 2011; Empeño, Raming, Irwin, Nelesen, & Lloyd, 2011; Smith Barusch, & Spaid, 1991). These gains appear to hold constant, regardless of whether the services are provided locally (e.g., on-site support groups) or through telephone support groups (Brown et al., 1999). Moreover, it may be important to continue outreach even after their caregiving role has ceased due to the passing of the patient. One study of family caregivers in palliative care found that bereavement was associated with increased alcohol consumption (Hauser & Kramer, 2004), and another identified that the negative psychological effects of caregiving (e.g., depression, loneliness) persisted for as long as three years after their caregiver responsibilities had ceased (Robinson-Whelen, Tada, MacCallum, McGuire, & Kiecolt-Glaser, 2001).
The provision of proper education and continued support for caregivers of ICU patients requires attention. These findings are an important preliminary step toward understanding the long-term consequence of caregiving as it pertains to the development of problematic alcohol use. Our findings, which echo results and subsequent “calls to action” expressed in similar research, should prompt medical professionals and policy administrators to evaluate the structure and efficacy of their communication with future caregivers before they leave the ICU. At a minimum, medical personnel should strive to establish realistic expectations about the needs of the patient and their role as a caregiver. Further, clinicians and medical practitioners should examine the support services offered to caregivers, both during their time in the ICU and, more importantly, after they have left the structured confines of the hospital.
Limitations
The present study has several limitations. First, despite the use of psychometrically validated measures of caregiver burden and alcohol use, the study may be limited by unavoidable self-report bias inherent in questionnaires. This limitation may also have influenced other predictive measures (e.g., caregiver differential), which were assessed and calculated from self-report, single item questions. However, both the baseline and follow-up assessments were administered by trained clinical researchers who had built rapport with these caregivers during their stay in the ICU and were able to clarify any questions or concerns. Future studies should incorporate the use of standardized measures, as well as in-person interviews and observational methods, when applicable, to help in reducing report biases and allow for the integration of information from different perspectives. For example, future research should consider structured clinical interviews (e.g., SCID-5-RV; First, Williams, Karg, & Spitzer, 2014) or retrospective recall for formal assessment of alcohol consumption (e.g., Alcohol Timeline Follow-Back; Sobell & Sobell, 1992). Second, it is also important to note that a good portion of the variance in alcohol use outcomes at 6 months was not explained by our model, with baseline alcohol use being the largest and most influential predictor of subsequent problematic alcohol use. Interpretation of the findings regarding the role of caregiving expectation should consider these factors. Third, data pertaining to the severity of the patients’ injury were not available. However, severity of injury may have influenced both the anticipation of time spent in the caregiving role and, subsequently, the amount of caregiver burden, caregiving differential scores, and alcohol use. For example, if a patient had a less severe injury, it may have made it more difficult for the caregiver to adequately assess the amount of time they would spend in the caregiving role, which, in turn, influenced their drinking. Further, in regard to intervention, the severity of the patients’ injury may play a critical role in determining the need for caregiver support. For example, it may be that caregivers of patients with more severe injury require more long-term support services, in addition to establishing realistic expectation about caregiving. Future projects targeting caregivers should make sure to include some measure of patient injury severity, such as the Injury Severity Score (ISS; MacKenzie, Shapiro, & Eastham, 1985). Fourth, completed 6-month follow-ups were provided by only 65.3% of caregivers. However, participant baseline characteristics were available for all 124 caregivers, and did not differ significantly on any important demographic (e.g., age, ethnicity) or pertinent nondemographic (e.g., baseline AUDIT-C score) variables as a function of follow-up completion. Moreover, due to this attrition, the final sample available for analyses was relatively small (n = 81); larger scale investigations are required to replicate and extend these findings. Last, despite inclusion criteria stipulating that only caregivers of patients expected to survive for ≥96 hrs were eligible to participate in the study, 16 participants experienced the loss of their loved one at some point between baseline assessment and their 6-month follow-up. While these individuals were given a bereavement protocol in place of the standard follow-up assessment, which included other measures (e.g., assessing complicated grief rather than caregiver burden), they were excluded from the current analyses. Given past research indicating that the negative psychological consequences of caregiving persist even after that role has ceased (Robinson-Whelen et al., 2001), future studies evaluating bereaved caregivers should include measures of alcohol use to assess the prevalence of problematic drinking among those individuals.
Despite these limitations, findings from the present study add to the literature in a number of ways. First, the prospective nature of the study was a considerable strength, allowing the researchers to gauge “real time” assessments of expectations at baseline and control for caregivers’ baseline levels of alcohol use during the analyses. Second, the study included a heterogeneous sample of both caregivers, in terms of their relationship to the patient (e.g., spouse, friend, child), and patients, in terms of the reason for their admission to the ICU (e.g., chronic illness, traumatic injury). Previous research typically has focused on a specific population of caregivers and/or patients, limiting the generalizability and external validity of their findings. Last, because the study focused on caregiver expectations, as opposed to an abstract construct (e.g., emotionality) or a clinical response to trauma (e.g., posttraumatic stress), points for intervention and solutions for ameliorating the underlying issues may be more direct and clear for medical providers and practitioners.
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Submitted: November 3, 2016 Revised: April 5, 2017 Accepted: April 6, 2017
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Record: 23- Drinking initiation and problematic drinking among Latino adolescents: Explanations of the immigrant paradox. Bacio, Guadalupe A.; Mays, Vickie M.; Lau, Anna S.; Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013 pp. 14-22. Publisher: American Psychological Association; [Journal Article] Abstract: Studies indicate that U.S.-born Latino teens exhibit higher rates of alcohol use compared with their foreign-born counterparts. Different hypotheses have been advanced to explain the mechanisms underlying this immigrant paradox, including the erosion of protective cultural factors across generations and increased exposure to risky peer environments in the United States. The present study examined whether the immigrant paradox applies to drinking initiation and problematic drinking among Latino adolescents, and tested whether generational differences in family protective factors and peer risk factors might explain the immigrant paradox. A nationally representative sample of Latino teens (N = 2,482) of Cuban, Mexican, and Puerto Rican origin from 3 immigrant generations (21% first generation, 33% second generation, and 46% third and later generations) was obtained from the National Longitudinal Study of Adolescent Health. Logistic and negative binomial regression models indicated that early drinking initiation and problematic alcohol use were more prevalent among later-generation youth, supporting the immigrant paradox. Erosion of family closeness and increased association with substance-using peers mediated the relationship between generation and alcohol use patterns in this sample. Results provide support for culturally sensitive interventions that target peer perceptions of substance use and bolster protective family values among Latino adolescents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Drinking Initiation and Problematic Drinking Among Latino Adolescents: Explanations of the Immigrant Paradox
By: Guadalupe A. Bacio
Department of Psychology, University of California, Los Angeles;
Vickie M. Mays
Departments of Psychology and Health Services, University of California, Los Angeles, and UCLA Center on Research, Education, Training and Strategic Communication on Minority Health Disparities
Anna S. Lau
Department of Psychology, University of California, Los Angeles
Acknowledgement: This work was supported by funding from the National Institutes of Health, National Center on Minority Health and Health Disparities (MD 00508). We thank Susan D. Cochran, PhD, for her assistance in data management. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.
Alcohol continues to be the most abused substance among adolescents in the United States. By twelfth grade, 72% of adolescents report having consumed alcohol, 55% report having been drunk, and 25% report binge drinking in the past 2 weeks (Johnston, O'Malley, Bachman, & Schulenberg, 2010b). The public health impact of teen drinking is highlighted by the array of alcohol-related problems reported by young drinkers, such as interpersonal problems, impaired school and work performance, risky sexual behaviors, and drunk driving (Brown et al., 2008; National Institute on Alcohol Abuse & Alcoholism, 2006; Office of the Surgeon General, 2007; Windle & Windle, 2006).
Latino adolescents exhibit the second highest rates of alcohol use, closely following non-Hispanic White teens (Johnston, O'Malley, Bachman, & Schulenberg, 2010a). One of the most consistent factors associated with drinking patterns among Latino teens is nativity. U.S.-born Latino adolescents report higher levels of alcohol use compared with their first-generation immigrant counterparts (Gil, Wagner, & Vega, 2000; Guilamo-Ramos, Jaccard, Johansson, & Turrisi, 2004; Vega & Gil, 1998). Indeed, nativity-based disparities are apparent across many health outcomes, including substance abuse and mental disorders (Alegria et al., 2008). However, immigrants are often exposed to stress or trauma before and during the migration process, commonly settle in impoverished neighborhoods, and confront greater language barriers compared with their U.S.-born counterparts (Guarnaccia & Lopez, 1998; Pumariega, Rothe, & Pumariega, 2005). The advantaged health status of first-generation Latinos has come to be known as the “immigrant paradox” (Markides & Coreil, 1986; Vega & Sribney, 2011).
Although the mechanisms underlying the immigrant paradox are not well understood, the literature has advanced different hypotheses. Proposed explanations include acculturation stress theory, assimilation theory, the healthy immigrant hypothesis, erosion of cultural values, and increased exposure to risky environments. The acculturative stress framework posits that the strain resulting from the challenges that Latino youth encounter as they adapt to the host culture generates stressful situations that elicit substance use as a maladaptive stress management response (Gil et al., 2000). Assimilation theory proposes that as Latino teens assimilate to mainstream culture, their drinking patterns will change to reflect the norms of the host culture (Caetano & Clark, 2003). The healthy immigrant effect explains that healthier people are more likely to successfully immigrate to the United States (Crimmins, Soldo, Kim, & Alley, 2005) and may appear healthier than their U.S.-born counterparts.
Some suggest that erosion of protective features of the culture of origin accounts for increased risk across generations (Barrera, Gonzales, Lopez, & Fernandez, 2004; Mogro-Wilson, 2008). For example, parenting practices and relationships among Latino families are organized by values highlighting the centrality of family integrity. Familismo is a dynamic construct often defined as a normative set of values endorsed by Latinos that encompasses several facets. These include a sense of obligation to provide instrumental support to the family, an edict that family expectations should guide behavior, and an implicit sense that emotional support must be cultivated within the family (Germán, Gonzales, & Dumka, 2009; Sabogal, Marin, Otero-Sabogal, Vanoss Marin, & Perez-Stable, 1987). Orientation toward traditional family values has been found to be protective against externalizing behaviors (Germán et al., 2009; Gonzales et al., 2008), including alcohol and drug use (Castro, Stein, & Bentler, 2009; Gil et al., 2000). However, familismo decreases across generations as Latino teens acculturate, and this decline appears related to increased alcohol use (Gil et al., 2000). As family values change across generations, so, too, may parenting practices. Parental monitoring of adolescents decreases with acculturation among Latino parents (Driscoll, Russell, & Crockett, 2008; Mogro-Wilson, 2008), and decreased monitoring is associated with increased alcohol use among Latino adolescents (Driscoll et al., 2008; Mogro-Wilson, 2008). Thus, the erosion of protective family practices involving closeness and monitoring may explain generational differences in drinking among Latino youth.
Another explanation for the immigrant paradox is that U.S.-born Latino adolescents are disproportionately exposed to environmental conditions that predispose risk, such as substance-using peers (Gil et al., 2000; Lopez et al., 2009; Prado et al., 2009). During adolescence, peer networks become central as teens begin to seek individuation (Brown et al., 2008). Teens are more likely to engage in risky behaviors, including alcohol use, if they associate with deviant peers (Barrera et al., 2004; Brown et al., 2008). It is plausible that immigrant teen social networks present less peer risk than those of U.S.-born Latino youth. Immigrant Latino adolescents are more likely to affiliate with other immigrant youth because of school placements organized by English proficiency (Carhill, Suárez-Orozco, & Paez, 2008) and preferences for Spanish-speaking peers (Carhill et al., 2008). Immigrant and Spanish-speaking Latino youth are less likely to use alcohol (Marsiglia & Waller, 2002). Conversely, U.S.-born Latino teens are more likely to have English-speaking U.S.-born peers who report greater use of alcohol and drugs (Allen et al., 2008). Thus, deviant or substance using peer networks may represent a social risk factor explaining an immigrant paradox in teen drinking.
The first aim of this study was to examine whether the immigrant paradox was present in Latino teens' drinking initiation and problematic drinking using a nationally representative sample of Latino teens. Because most studies evaluating the immigrant paradox have examined nativity, contrasting U.S.-born with foreign-born Latinos, little is known about how drinking patterns among third- and later-generation Latino youth compared with second- and first-generation adolescents. To that end, we examined differences among three generations of Latino youth.
The second aim of this study was to examine the contribution of two hypothesized mechanisms proposed to explain the immigrant paradox, namely, erosion of cultural family practices and increased exposure to risky behaviors. First, the cultural erosion hypothesis was examined using two relevant protective factors, namely, parental monitoring and family closeness, as putative mediators. Second, we examined the role of exposure to risky peer environments using an index of association with substance-using peers as a putative mediator. We predicted that immigrant youth may be less likely than later-generation youth to initiate drinking and experience alcohol-related problems because they benefit from more family closeness, parental monitoring, and prosocial peer networks.
Method Sample and Procedure
The National Longitudinal Study of Adolescent Health (Add Health) is a nationally representative study of health and risk behavior among U.S. adolescents in Grades 7 through 12 (Harris et al., 2008). Add Health utilized a multistage and stratified sampling frame that included all high schools in the United States. A random sample of 80 high schools and their major middle-school feeders were selected for participation. Students completed a self-administered questionnaire during the period 1994 to 1995. A core sample of 12,105 adolescents was selected to participate in home interviews conducted between April and December of 1995. A resident parent, usually the mother, also completed an interview (Harris et al., 2008).
Study Sample
This study used a subsample of Add Health Wave I participants who identified as Latino or Hispanic (N = 2,482); of Mexican (62%), Cuban (18%), and Puerto Rican (20%) origin; who spoke English (53%) or Spanish (47%); and who indicated whether or not they had consumed alcohol in their lifetime. All items were selected from the adolescent interview unless otherwise noted.
Measures
Generational status
Immigrant generation was determined using parent and adolescent responses regarding their respective country of birth. Adolescents who reported being foreign-born were classified as first generation. Teens who reported being U.S.-born and whose parent reported being foreign-born were categorized as second generation. Adolescents who reported being U.S.-born and whose parent reported being also U.S.-born were classified as third and later generation.
National origin
All participants indicated that they were of Latino or Hispanic origin. In addition, to be included in the current study, adolescents self-identified as Mexican/Mexican American/Chicano, Cuban/Cuban American, or Puerto Rican.
Language use at home
Adolescents indicated the usual language spoken at home by choosing English or Spanish.
Parental alcohol use
Parents were asked to indicate how often in the past year they had a drink on a 6-point scale ranging from never (1) to nearly every day (6).
Family socioeconomic status
Parents were asked to indicate their level of educational attainment (less than high school, high school or equivalent, some college, or college graduate and beyond). Parents also reported their annual family income (less than $14,999, $15,000 to $29,999, $30,000 to $44,999, $45,000 to $59,999, and $60,000 or above).
Family structure
Based on teen reports on multiple items regarding household composition, family structure was coded into one of five categories (two biological parents, at least one non-birth parent identified as a parent figure [step, adoptive, grandfather, etc.], single parent, and other [foster home, no identified parent figures, etc.]).
Initiation of drinking
Adolescents indicated whether or not they had ever had a drink of beer, wine, or liquor more than two or three times in their life. The item directed teens to exclude a sip or a taste of someone else's drink.
Problematic alcohol use
For those reporting alcohol initiation, the frequency of alcohol-related problems in the past year was assessed by asking adolescents how many times, as a result of drinking, they “got into trouble with their parents,” “had problems at school or with their schoolwork,” “had problems with friends,” “had problems with someone they were dating,” “did something they later regretted,” “were hung over,” “were sick to their stomach or threw up,” “got into a sexual situation they later regretted,” and “got into a physical fight.” Responses ranged on a 5-point scale from 0 times to 5 or more times in the past year, and were summed for a maximum total of 45 points (α = .85).
Perceived family closeness
Teens were asked, “How much do you feel that…” “your parents care about you,” “people in your family understand you,” “you and your family have fun together,” and “your family pays attention to you”? Answers ranged from not at all (1) to very much (5), for a possible total score of 20 points. Higher scores indicated greater family closeness (α = .76).
Perceived parental monitoring
Adolescents were asked, “Do your parents let you make your own decisions about…” “the time you must be home on weekend nights,” “the people you hang around with,” “what you wear,” “how much TV you watch,” “which TV programs you watch,” “what time you go to bed on week nights,” “what you eat”? Answers were dichotomous for a possible summed total score of seven points. Higher scores indicated greater degree parental monitoring (α = .65).
Association with substance-using peers
Adolescents were asked, “Of your best friends, how many…” “drink alcohol at least once a month,” “smoke a cigarette at least once a day,” and “use marijuana at least once a month”? Answers ranged from 0 to 3, for a total summed score of 9 points. Higher scores indicated greater association with substance-using peers (α = .76).
Missing Data
Missing data ranged from 0% to 32%, depending on the variable, with a mean of 6.21% across all study variables. Items obtained from the parent questionnaire, including family income and parental education, contained the highest percentage of missing data (18% and 32%, respectively). Listwise deletion procedures are not recommended, as this approach may yield biased results; therefore, multiple imputation (MI) was used to estimate missing data values (Rubin, 1987). Missing values were imputed by the command ice (imputation by chained equations; Royston, 2009) in Stata 10 (StataCorp, 2009) using equation models that combined relevant predictors in the data set previously identified using the command pred_eq (Medeiros, 2007). Twenty imputed data sets were created that were then combined using the command mim to generate estimates (Carlin, Galati, & Royston, 2008). MI is commonly used because it yields estimates averaged over the imputed data sets that reflect unbiased parameters and standard errors that take into account the uncertainty of using imputed missing values (Graham, Allison, & Gilreatch, 2007).
Analytic Strategy
First, the relationships between immigrant generation and drinking initiation and problematic alcohol use were examined to establish if the immigrant paradox was prevalent in each of these outcomes. Second, the mediating role of family closeness and parental monitoring, and association with substance-using peers, were tested in separate models to examine if each hypothesized mediator explained generational differences in each drinking outcome. Third, a multimediation model, including all proposed mediators, was conducted to ascertain if each hypothesis explained generational differences in the examined drinking outcomes over and above the others included in the model. Tests of mediation were conducted following the Baron and Kenny (1986) approach. Significance of mediation effects was determined using Sobel tests (Sobel, 1982). All models controlled for adolescents' gender, age, national origin, language used at home, parental alcohol use, family structure, and family socioeconomic status.
Logistic regressions were used to examine the direct and indirect effect of generation on likelihood of drinking initiation during adolescence. Numbers of alcohol-related problems were examined among youth who had started to drink (n = 1,537). Because the variance of this count variable is greater than its mean, negative binomial regression models were used. The alphas obtained in every negative binomial regression conducted were significantly greater than zero, indicating that negative binomial models provided better estimates than would have regular Poisson models. Ordinary least squares regressions were used to examine generational differences in the proposed mediators, all of which are continuous variables. All analyses used the appropriate survey weights to correct for design and sampling effects, as not doing so may yield biased parameter estimates (Chantala & Tabor, 1999). Add Health selected high schools with replacements from the Quality of Education Database as the basis for a stratified cluster sampling (Tourangeau & Shin, 1999) and adjusted individual weights for oversampling. Adolescents for whom weights were missing were excluded from analysis, as recommended by Chantala and Tabor (1999).
Results Sample Characteristics
Table 1 describes the sample used in this study. Twenty-one percent of the adolescents were first-generation immigrants, 33% were second-generation immigrants, and 46% were third-generation immigrants. Participants' age ranged from 11 to 21 years (M = 15.9, SD = 1.7) and 49% were female. Fifty-three percent were living with both biological parents, 48% of parents had not completed high school, and 59% reported a family gross income of $29,000 or less.
Sociodemographic Characteristics by Immigrant Generation Based on Weighted Analyses, Wave I Longitudinal Study of Adolescent Health
Generation and Drinking Initiation
As shown in Table 2, likelihood of alcohol initiation during adolescence increased with generation, F(2, 1000) = 18.58, p < .001. Second-generation teens were 2.77 times more likely and third-generation teens were 3.38 times more likely than first-generation teens to have started drinking. There was no significant difference in drinking initiation between the second and third generations. Age significantly predicted alcohol initiation in the expected direction, t(125) = 8.31, p < .001.
Weighted Odds Ratios (OR) for Each Mediation Model Predicting Drinking Initiation Among Latino Adolescents of Different Immigrant Generations, Wave I Longitudinal Study of Adolescent Health
Mediational Analyses for Drinking Initiation
Table 2 shows the results of mediational analyses for drinking initiation.
Erosion of cultural values hypothesis
Parental monitoring decreased across generations, F(2, 1000) = 3.49, p < .05, but was not significantly related to initiation and thus was not a mediator. Family closeness decreased across generations, F(2, 1000) = 3.43, p < .05, and significantly predicted initiation, t(125.6) = −4.36, p < .001. The effect of generation on initiation was attenuated after parental monitoring and family closeness were added to the model, F(2, 1000) = 14.67, p < .001. Partial mediation was confirmed using the Sobel test when comparing lifetime alcohol use between second and first generations, Z = 1.96, p < .05, and between third- and first-generation teens, Z = 2.06, p < .05.
Exposure to risky peer environment
Association with substance-using peers increased across generations, F(2, 1000) = 14.77, p < .001, and significantly predicted initiation, t(124.2) = 10.89, p < .001. The effect of generation on initiation was attenuated but remained significant after accounting for substance-using peers, F(2, 979.8) = 7.23, p < .001. Sobel tests determined that the effect of generation on drinking initiation was partially mediated by association with substance-using peers (second vs. first, Z = 3.49, p < .05; third vs. first, Z = 4.84, p < .05).
Multimediation
The effect of generation on lifetime alcohol use was attenuated but remained significant after introducing all mediators in the model, F(2, 1000) = 6.37, p < .05. Only family closeness, t(125.5) = −2.47, p < .05, and association with substance-using peers, t(124.4) = 9.79, p < .001, significantly predicted lifetime use. Sobel tests indicated that family closeness was not a significant mediator. However, association with substance-using peers partially mediated the relationship between generation and lifetime use (second vs. first, Z = 3.47, p < .05; third vs. first Z = 4.76, p < .05).
Generation and Problematic Alcohol Use
As shown in Table 3, among the subsample of youth who had initiated drinking, generation significantly predicted number of alcohol-related problems, F(2, 1000) = 5.32, p < .001. Third-generation youth reported a rate of alcohol related problems 1.84 times greater than first-generation teens, t(113.1) = 3.18, p < .001, and 1.48 times greater than second-generation adolescents, t(110.4) = 1.98, p < .05. There were no differences in problematic use between first- and second-generation teens, t(123.1) = 1.06, p > .05. Age was also related to increased rates of problematic alcohol use in the expected direction, t(124.8) = 3.11, p < .05.
Weighted Incidence Rate Ratios (IRR) for Each Mediation Model Predicting Problematic Alcohol Use Among Latino Adolescents of Different Immigrant Generations, Wave I Longitudinal Study of Adolescent Health
Mediational Analyses for Problematic Alcohol Use
Table 3 shows the results of mediational analyses for problematic alcohol use.
Erosion of cultural values hypothesis
There were no generational differences in parental monitoring among drinkers and, as such, it was ruled out as a mediator. Family closeness decreased across generations, F(2, 1000) = 4.90, p < .01, and significantly predicted the number of teen alcohol-related problems, t(122.5) = −4.77, p < .001. The effect of generation on alcohol-related problems was reduced but remained significant after accounting for monitoring and closeness, F(2, 1000) = 3.32, p < .05. Sobel tests determined that family closeness partially mediated the effect of immigrant generation on problematic alcohol use when comparing first- to third-generation adolescents, Z = 3.98, p < .05.
Exposure to risky peer environment
Association with substance-using peers increased across generations among adolescent drinkers, F(2, 1000) = 4.99, p > .05. Association with substance-using peers significantly predicted problematic alcohol use, t(118.3) = 9.95, p < .001. The effect of generation on problematic alcohol use was not significant when association with substance-using peers was introduced in the model, F(2, 1000) = 2.51, p < .001. The effect of generation on problematic alcohol use was fully mediated by substance-using peers (third vs. first, Z = 3.02, p < .05; third vs. second, Z = 2.23, p < .05).
Multimediation
Generation was not significantly related to problematic alcohol use after introducing all mediators in the model, F(2, 1000) = 1.93, p > .05. Family closeness, t(123.7) = −4.33, p < .001, and association with substance-using peers, t(118.6) = 10.50, p < .001, significantly predicted problematic alcohol use. Family closeness fully mediated the effect of generation on problematic alcohol use when comparing third- with first-generation teens, Z = 289, p < .05. Association with substance-using peers fully mediated the effect of generation on problematic alcohol use (third vs. first, Z = 3.16, p < .05; third vs. second, Z = 2.29, p < .05).
DiscussionThe first aim of this study was to examine the prevalence of the immigrant paradox in drinking initiation and problematic alcohol use among Latino adolescents of three immigrant generations. Variants of the immigrant paradox in these drinking patterns were identified. Consistent with previous studies, U.S.-born teens (second and third and later generations) were more likely to initiate drinking compared with immigrant adolescents (Gil et al., 2000; Guilamo-Ramos et al., 2004; Vega & Gil, 1998). However, teens whose parents are U.S.-born (third and later generations) were more likely to experience alcohol-related problems than adolescents whose parents were foreign-born (first and second generations). These findings suggest that nativity and immigrant generation are associated differently with varying drinking outcomes, and the results highlight the importance of assessing generation in addition to nativity when studying alcohol use among Latino teens in the United States. Relying solely on nativity may obscure important similarities and differences among generations of Latino teens. It is possible that assessing only nativity may miss sociocultural processes potentially encompassed by generation, such as acculturation status, enculturation status, or divergent cultural values.
The second aim of the study was to test the contributions of the erosion of cultural values hypothesis and the exposure to risky peer environment hypothesis in explaining generational differences in these drinking patterns. There was support for the hypothesis that the immigrant paradox is partly due to differences in family functioning across generations. Specifically, differences in family closeness across generations, but not parental monitoring, played an important role in explaining generational differences in drinking patterns. It is important to note that the indicator of parental monitoring used had low reliability and may not have captured the ways that parents in this sample exercise parental monitoring. The often-taxing work demands that disadvantaged Latino immigrant parents have to juggle may interfere with their ability to be present in their homes to closely supervise the activities of their offspring, and, as a result, this measure may not be the best indicator of care-giving quality or protective parenting practices.
The negative association of family closeness with alcohol use is consistent with the concept that familismo is protective against deviant behaviors (Castro et al., 2009; Gil et al., 2000; Gonzales et al., 2008). Nonetheless, differences in family closeness did not systematically explain the generational increases in drinking outcomes. Parents of first-generation teens are foreign-born and likely promote familismo more so than parents of third-generation teens who are U.S.-born. Consistently, the greater likelihood of drinking initiation and problematic drinking of third- compared with first-generation teens was partially explained by the generational decline in family closeness. However, decreases in family closeness between the first and second generations did not explain their differences in drinking initiation. Immigrant parents of first- and second-generation youth may support familismo in similar ways, and the increase in drinking initiation between these generations may be better explained by extrafamilial factors such as affiliation with substance-using peers. Similarly, the higher rates of problematic drinking of third- and later- compared with second-generation teens were not explained by differences in family closeness. Although the erosion of family closeness across generations indeed impacts teen alcohol outcomes, it does not fully account for the immigrant paradox in drinking patterns.
Findings support the hypothesis that increased exposure to risky peer environments, through association with substance-using peers, partly explicates the immigrant paradox in drinking among Latino youth. Consistent with other studies (Brown et al., 2008; Lopez et al., 2009; Windle, 2000), adolescents of later generations reported associating with more substance-using peers, and this was related, in turn, to higher likelihood of drinking initiation and problematic alcohol use. However, the effect of association with substance-using peers differed by outcome. Increased association with substance-using peers partially explained the generational increases in drinking initiation. Once adolescents started drinking, risk exposure had a stronger effect such that association with substance-using peers fully mediated the relationship between generation and problematic drinking.
The purported mediators were simultaneously tested as explanations of the generational differences in drinking. Affiliation with substance-using peers was the strongest, albeit partial, explanation of increased drinking initiation among later generations. This robust effect of generation on drinking initiation underlines the importance of continuing to investigate this relationship to inform prevention efforts for Latino adolescents. Similarly, increased association with substance-using peers and decreased family closeness simultaneously explained the significant increase in problematic drinking of third- compared with first-generation teens. These results are consistent with other studies that have found that orientation toward family values buffers the effect of associating with substance-using peers (Germán et al., 2009; Prado et al., 2009). However this study suggests that the protective role of family closeness may be particularly important for first-generation teens in preventing problematic drinking (Wagner, 2003), even after accounting for the strong effects of associating with substance-using peers. It is important to consider that the centrality of family and peer networks changes during adolescence and that the value ascribed to each may differ across generations. It is possible that the comparative advantages of the second and third generations over the first generation, such as speaking English and being U.S. citizens, may decrease the importance that family closeness plays in their development. For these later generations, a better point of intervention might be peer-focused. For first-generation teens, on the other hand, maintaining family closeness may be more adaptive as they enter a new culture and face the adaptation challenges together.
The results from this investigation should be taken with caution due to several limitations. Other plausible explanations for the immigrant paradox were not examined. For instance, greater perceived discrimination associated with nativity and longer residence in the United States (Cook, Alegria, Lin, & Guo, 2009; Córdova & Cervantes, 2010) may also account for the increase in alcohol problems in later-generation Latino youth (Pérez, Fortuna, & Alegria, 2008). Thus, although the putative mediators tested in this study are important, these factors may combine with other risk mechanisms to explain the immigrant paradox.
As a cross-sectional study, it is not possible to determine causality or infer directionality of influence with certainty. For example, the association between family closeness and drinking may signify that teens who drink are more likely to become estranged from their families. Similarly, the directionality of association with substance-using peers may be reversed, such that teens who drink are more likely to select friends who drink. Furthermore, family closeness only approximates one facet of familismo and does not include the other two factors identified by Sabogal and colleagues (1987), namely, sense of obligation to provide support to the family and following family expectations of behaviors. Despite that Sabogal and colleagues (1987) used a diverse sample of Latino individuals of Central American, Cuban, and Mexican origin, it is possible that Latino subgroups may differ in how they interpret and endorse different facets of familismo as a construct. Future studies would benefit from specific instruments that directly measure this cultural construct.
The sample size of our study allowed us to examine only the influence of generation for three major Latino subpopulations of Cuban, Mexican, and Puerto Rican origin. However, our analyses do not speak to possible differences in risk patterns among Latino subgroups. The systematic advantages and disparities between Latinos of Mexican, Puerto Rican, and Cuban origin in language proficiency, migration status, and socioeconomic status may modify how the immigrant paradox in drinking patterns manifests among each group. Moreover, our findings may not be applicable to other Latino populations in the United States. Future studies would benefit from the use of prospective designs with samples that include other Latino subgroups. Limitations notwithstanding, this study represents an important first step toward testing a theory-driven model of alcohol use initiation and alcohol problems in Latino youth.
In sum, drinking initiation and problematic alcohol use among Latino adolescents, as well as the contribution of the tested explanations, differed across generations. This study highlights the importance of assessing beyond the dichotomous indicator of nativity and considering the effect of immigrant generation when studying alcohol use among Latino teens. Further, the results indicate that multiple factors influence alcohol use patterns among Latino adolescents and operate in tandem to explain the immigrant paradox. Findings suggest that effective preventions to delay drinking initiation among Latino teens should target perceptions of peer alcohol and drug use. These results also offer support to culturally sensitive interventions geared at Latino adolescents that bolster family closeness and strengthen perception of family support (Pantin et al., 2009), which may help reduce problematic alcohol use through the transition to adulthood.
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Submitted: October 10, 2011 Revised: May 8, 2012 Accepted: August 14, 2012
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Record: 24- Driving after use of alcohol and marijuana in college students. McCarthy, Denis M.; Lynch, Andrea M.; Pederson, Sarah L.; Psychology of Addictive Behaviors, Vol 21(3), Sep, 2007 pp. 425-430. Publisher: American Psychological Association; [Journal Article] Abstract: Driving after use of marijuana is almost as common as driving after use of alcohol in youth (P. M. O'Malley & L. D. Johnston, 2003). The authors compared college students' attitudes, normative beliefs and perceived negative consequences of driving after use of either alcohol or marijuana and tested these cognitive factors as risk factors for substance-related driving. Results indicated that youth perceived driving after marijuana use as more acceptable to peers and the negative consequences as less likely than driving after alcohol use, even after controlling for substance use. Results of zero-inflated Poisson regression analyses indicated that lower perceived dangerousness and greater perceived peer acceptance were associated with increased engagement in, and frequency of, driving after use of either substance. Lower perceived likelihood of negative consequences was associated with increased frequency for those who engage in substance-related driving. These results provide a basis for comparing how youth perceive driving after use of alcohol and marijuana, as well as similarities in the risk factors for driving after use of these substances. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Driving After Use of Alcohol and Marijuana in College Students
By: Denis M. McCarthy
Department of Psychological Sciences, University of Missouri–Columbia;
Andrea M. Lynch
Lynch Graduate School of Education, Boston College
Sarah L. Pedersen
Department of Psychological Sciences, University of Missouri–Columbia
Acknowledgement: This research was supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant R03 AA13399 to Denis M. McCarthy and by NIAAA Grant T32 AA13526.
In 2003, motor vehicle accidents were the leading cause of death in college-age youth in the United States (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2003). In 2002, 29% of drivers aged 15 to 20 years killed in traffic accidents were intoxicated (National Highway Traffic Safety Administration, 2003). More than a third (35.5%) of U.S. college student drivers reported drinking and driving in the past month (Wechsler, Lee, Nelson, & Lee, 2003).
Marijuana is the most commonly used illicit drug by U.S. youth. In a nationally representative survey, more than half (53.8%) of those age 18–25 reported lifetime use of marijuana (Office of Applied Studies, 2004). Marijuana was the second most frequently used drug, after alcohol, in samples of reckless drivers (Brookoff, Cook, Williams, & Mann, 1994) and of those involved in vehicle accidents resulting in injury or fatality (Soderstrom, Dischinger, Kerns, & Trifillis, 1995; Terhune et al, 1992). The Monitoring the Future study found that the percentage of U.S. high school seniors who received tickets or had accidents after use of marijuana was comparable to that of alcohol (O'Malley & Johnston, 2003). Rates of self-reported driving after use were also similar for alcohol and marijuana. Given the differences in prevalence of use, these results suggest that youth are relatively more likely to drive after using marijuana than alcohol.
Empirical research has long documented impairment in driving abilities from use of marijuana (Crancer, Dille, Delay, Wallace, & Haykin, 1969; Moskowitz, 1985). Recent studies have demonstrated that marijuana can increase brake latency (Liguori, Gatto, & Robinson, 1998), lateral position errors, and distance variability (Ramaekers, Robbe, & O'Hanlon, 2000; Robbe, 1998) in simulated and closed-road driving tasks.
Considerable research has focused on identifying individual difference factors associated with drinking and driving behavior. For example, disinhibited personality constructs have been correlated with drinking and driving behavior and arrest (Cavaiola, Strohmetz, Wolf, & Lavender 2003; Donovan, Queisser, Umlauf, & Salzberg, 1986; Turrisi, Jaccard, & McDonnell, 1997). Cognitive factors, such as perceived norms (Armitage, Norman, & Connor, 2002), the perceived dangerousness of drinking and driving (Grube & Voas, 1996), and risk appraisal (Gerrard, Gibbons, Benthin, & Hessling, 1996), have also been found to be associated with increased likelihood of drinking and driving in adolescents and young adults.
In contrast, relatively little is known about risk factors and perceptions of driving after use of marijuana. A study of intravenous drug users found that alcohol was rated as the most dangerous drug to use prior to driving, whereas marijuana was rated as the least dangerous (Darke, Kelly, & Ross, 2004). Studies of marijuana users found that they did not perceive marijuana use as affecting their driving ability (Aitken, Kerger, & Crofts, 2000) and perceived driving after use as less impairing than driving after drinking (Terry & Wright, 2005). It is unclear if perceptions are similar for nonusers or if these perceptions are associated with marijuana-related driving.
The present study was designed to improve our understanding of college students' perceptions of driving after use of marijuana. Our first goal was to compare perceptions of driving after marijuana use with perceptions of driving after use of alcohol. Parallel questions assessed normative beliefs, attitudes, and perceived negative consequences for driving after use of alcohol and marijuana. We hypothesized that participants would rate driving after use of marijuana as more acceptable to peers, less dangerous, and less likely to have negative consequences than driving after use of alcohol. Analyses were also conducted controlling for frequency of alcohol and marijuana use.
A second goal was to test cognitions as risk factors for substance-related driving and to evaluate differences in prediction of driving after use of alcohol and marijuana. We hypothesized that greater acceptance by peers, lower perceived dangerousness, and lower perceived probability of negative consequences would be associated with increased likelihood and frequency of self-reported driving after use of alcohol and marijuana. Frequency of use and gender were included as covariates in these analyses.
Method Participants
Participants were recruited from introductory psychology classes at the University of Missouri-Columbia. The sample (N = 599) was 59% women, with a mean age of 18.54 years (SD = 0.86). The sample was primarily Caucasian (87%), with 7% African American, 3% Asian American, and 3% of mixed or other race; 3% reported their ethnicity as Hispanic.
Procedures
Participants were recruited using the introductory psychology subject pool. Data were collected in groups of 10–25. Participants received partial credit toward meeting a research requirement for their introductory psychology course for participating. Procedures were approved by the University of Missouri-Columbia Institutional Review Board.
Measures
Demographic information
A self-report questionnaire was used to collect demographic information, including age, gender, religion, and ethnicity.
Normative beliefs
Drinking and driving cognition questions were adapted from prior studies (Grube & Voas, 1996) and have been used in previous research in our laboratory (McCarthy, Pedersen, Thompsen, & Leuty, 2006; McCarthy, Pedersen, & Leuty, 2005). For normative beliefs, participants were asked how many (0–3) of their three closest friends disapprove of drinking and driving and how many would refuse to ride with a driver who had been drinking. Parallel questions were used to assess normative beliefs about driving after use of marijuana. Items were recoded so that higher scores indicated greater acceptance of substance-related driving. Internal consistency was .80 for alcohol questions and .91 for marijuana questions.
Attitudes
Three questions were used to assess attitudes towards drinking and driving. These questions asked participants how dangerous they think it is to drive within 2 hours of consuming one drink, three drinks, and five or more drinks. Questions used a four-point Likert scale and were coded so that higher scores indicated lower perceived dangerousness. Internal consistency in this sample was .83. For driving after marijuana use, a single question was used, asking how dangerous it is to drive within 2 hours of using marijuana.
Perceived negative consequences
For both alcohol and marijuana, four questions asked participants the likelihood a driver their age would be stopped by police, be breath or drug tested, be arrested, and have an alcohol- or marijuana-related accident. Questions used a four-point Likert scale and were coded so that higher scores indicated lower perceived probability of negative consequences. A mean composite was used for study analyses. Internal consistency was .84 for the alcohol questions and .90 for the marijuana questions.
Alcohol and marijuana use
The Drinking Styles Questionnaire (Smith, McCarthy, & Goldman, 1995) was used to assess alcohol use behavior. This measure has demonstrated good reliability and validity in adolescent and college samples (McCarthy, Miller, Smith, & Smith, 2001; Smith et al., 1995). In the present study, drinker/nondrinker status, past month quantity and frequency of use, and past month frequency of heavy drinking were used as measures of alcohol involvement. Similar questions were used for marijuana use. Questions assessed lifetime use of marijuana, age of first use, and frequency of use in the past year and month.
Driving after substance use
Drinking and driving was assessed with three open-ended questions asking participants to report how many times in the past 3 months they had driven within 2 hours of drinking one drink, three drinks, and five or more drinks. Driving after use of marijuana was assessed with a single question asking how many times participants had driven within 2 hours of smoking marijuana in the past 3 months.
Results Substance Use and Driving Behavior
Table 1 presents descriptive statistics for substance use and driving after use by gender. Comparisons across gender were made using either chi-square or t tests. Men were more likely to report use of marijuana and to drive after use of alcohol or marijuana. Men also reported higher frequency and quantity of alcohol use.
Descriptive Statistics for Alcohol and Marijuana Use and Driving After Use
Forty-three percent of the sample reported driving after drinking, whereas 13% reported driving after use of marijuana. However, these differences may be a function of differences in rates of current use. Of current drinkers, 55% reported driving after alcohol use in the past 3 months, whereas 47% of current marijuana users reported driving after smoking marijuana.
Alcohol and marijuana use were associated, χ2(1, N = 599) = 44.18, p < .01, with current drinkers more likely to use marijuana in the past month (30%) than nondrinkers (2%). There was also an association between driving after marijuana use and driving after one drink, χ2(1, N = 599) = 66.66, p < .01; three drinks, χ2(1, N = 599) = 67.73, p < .01; and five or more drinks, χ2(1, N = 599) = 61.08, p < .01.
Cognitions About Driving After Use
Table 2 presents correlations between substance use and cognitions about driving after use. Greater alcohol use was associated with perceiving drinking and driving as more acceptable to peers and less dangerous. The perception of negative consequences of drinking and driving as less likely was only weakly correlated with greater quantity of alcohol use. For cognitions about driving after marijuana use, frequency of use was associated with all driving cognition variables.
Correlations Between Substance Use and Driving Cognitions
Repeated measures analyses of variance were then used to compare cognitions for driving after use of alcohol with those for marijuana. In each analysis, substance type (marijuana, alcohol) was used as a within-subjects factor and gender as a between-subjects factor. For normative beliefs, there was a significant main effect of substance type, F(1, 597) = 62.17, p < .01; η2 = .10, with participants perceiving their peers as being more accepting of driving after use of marijuana than alcohol. There was a main effect of gender, F(1, 597) = 6.18, p < .05; η2 = .01, with men rating both behaviors as more acceptable. There was no Substance Type × Gender interaction. When frequency of marijuana and alcohol use were added to the analysis, the main effect of substance type was not as strong but remained significant, F(1, 595) = 6.11, p < .05; η2 = .01.
For perceived negative consequences, there was a main effect of substance type, F(1, 597) = 240.54, p < .01; η2 = .29, with participants perceiving negative consequences to be less likely for driving after use of marijuana than alcohol. There was a main effect of gender, F(1, 597) = 11.38, p < .01; η2 = .02, with men rating consequences for both behaviors as less likely. There was no Substance Type × Gender interaction. When controlling for alcohol and marijuana use, the main effects of substance type, F(1, 595) = 9.04, p < .05; η2 = .02, and gender, F(1, 595) = 5.79, p < .05; η2 = .01, remained significant.
We then compared the perceived dangerousness of driving after one drink, three drinks, and five drinks with the perceived dangerousness of driving after use of marijuana. Driving after use of marijuana was rated as more dangerous than driving after one drink, F(1, 597) = 599.29, p < .01; η2 = .51, and slightly more dangerous than three drinks, F(1, 597) = 4.20, p < .05; η2 = .01, but less dangerous than driving after five drinks, F(1, 597) = 366.42, p < .01; η2 = .39. There were significant main effects of gender for each analysis (all ps < .01), indicating that men viewed both behaviors as less dangerous. No Substance × Gender interactions were significant. The pattern of results was the same when frequency of alcohol and marijuana use were included as covariates, with driving after marijuana use rated as more dangerous than driving after one, F(1, 595) = 393.91, p < .01; η2 = .41, and three, F(1, 595) = 15.74, p < .01; η2 = .03, drinks, but less dangerous than after five drinks, F(1, 595) = 54.05, p < .01; η2 = .09.
Cognitions as Predictors of Driving After Use
We then tested whether cognitions were associated with driving after use of alcohol and marijuana. We estimated zero-inflated Poisson regression models using Mplus 3 (Muthén & Muthén, 2004). This model is appropriate when the dependent variable is a count variable with a high proportion of zero values. The dependent variable was number of times driving after use of alcohol or marijuana in the past 3 months. Mplus estimates two components in this type of model. The first, a zero-inflation component, estimates the odds of being in the zero class, or of not engaging in the behavior. This is similar to logistic regression, and an odds ratio is obtained for each independent variable. To simplify reporting, odds ratios were inverted so that higher values indicated greater likelihood of engaging in the behavior. The second component of the model provides a Poisson regression coefficient of the association between the independent variables and frequency of the dependent variable for those able to assume nonzero values. This coefficient is used to calculate the predicted rate of increase in the dependent variable for a one-unit increase in each independent variable (Cohen, Cohen, West, & Aiken, 2003).
For each model, frequency of substance use (either alcohol or marijuana), gender, and all three cognition variables were included as independent variables. For drinking and driving, the pattern of results was the same when each of the three drinking and driving variables (after one, three, or five drinks) was used as the dependent variable. Results are presented for driving after three drinks. For attitudes, perceived danger of driving after three drinks was used, as this variable was most similar to the parallel question for marijuana.
Table 3 presents odds ratios and predicted rate for substance use frequency and cognition variables. Frequency of substance use was associated with engagement and increased frequency of driving after use of either substance. Gender was related only to frequency of driving after use of marijuana. Lower perceived dangerousness and greater perceived peer acceptance were uniquely associated with both increased likelihood and increased frequency of driving after use of either substance. Lower perceived likelihood of negative consequences was associated with increased frequency of driving after use of either substance but not with engagement in either behavior.
Zero-Inflated Poisson Regression Analyses of Driving After Use of Alcohol and Marijuana
DiscussionOne goal of this study was to compare students' perceptions of driving after drinking with those of driving after the use of marijuana. Previous studies (Terry & Wright, 2005) demonstrated that marijuana users perceive driving after smoking marijuana as less impairing than driving after drinking. Our results support this finding, as marijuana use was strongly correlated with cognitions about driving after use. However, our results also indicate that college students in general perceived driving after smoking marijuana as more acceptable to their peers and the negative consequences to be less likely, even after controlling for frequency of use of these substances. When comparing perceived dangerousness of driving after marijuana use to driving after specific amounts of alcohol, youth viewed driving after marijuana use as slightly more dangerous than driving after three alcoholic drinks.
Our results also support substance-related driving cognitions as risk factors for driving after use of either alcohol or marijuana. Despite mean differences between cognitions, results were consistent for driving after use of alcohol and marijuana. Normative beliefs and attitudes had unique associations with both engagement in, and frequency of, driving after use of either substance. For perceived negative consequences, youth who engaged in these behaviors and viewed the negative consequences as less likely reported greater frequency of driving after use.
There are several reasons why youth may perceive driving after use of marijuana as more acceptable and the negative consequences less likely than those of drinking and driving. For over 20 years, the dangers of driving after use of alcohol have been the subject of public advertising campaigns and the focus of legal and public policy changes. Despite research evidence that marijuana impairs driving ability (Ramaekers et al., 2000), similar campaigns have only recently been targeted at driving after use of marijuana. The Office of National Drug Control Policy (2006) has expressed concern about the public image of marijuana as benign and includes information on marijuana's negative effects on driving skills in its youth media campaign.
In general, youth who reported greater involvement with a substance viewed driving after use as less risky. However, although perceived negative consequences were correlated with use of marijuana, these questions were largely not correlated with alcohol involvement. This may indicate that knowledge of the consequences of drinking and driving are not a function of personal use, perhaps due to the broader exposure to the potential consequences of drinking and driving in public discourse and media campaigns.
Differences between perceived negative consequences of driving after use of marijuana and alcohol may also reflect actual differences in legal enforcement between these two substances. The establishment of a per se standard has had a significant impact on reducing drinking and driving behavior (Giesbrecht & Greenfield, 2003). One mechanism by which such policy changes can influence behavior is by altering perceptions about the behavior, such as perceptions of risk and social norms (Greenberg, Morral, & Jain, 2004). In contrast, there is at present no parallel standard for marijuana use, in part due to lack of roadside and definitive testing of marijuana intoxication. Given this, it may be that youth are aware of these differences in enforcement standards, and their perceptions to some extent reflect actual lower probability of receiving negative consequences for driving after use of marijuana.
There are several limitations to the present study. The cross-sectional nature of the data limits inferences about the direction of the association between cognitions and driving behavior. To our knowledge, this study is the first to demonstrate associations between cognitions specific to driving after marijuana use and driving after such use. Finding cross-sectional associations, however, is only a first step toward demonstrating that these factors are important prospective predictors of behavior. Longitudinal studies would be required to examine whether these cognitions influence later substance-related driving behavior, driving behavior influences the development of cognitions, or a combination of both processes.
The sample used was of college students, which limits the generalizablity of findings to other populations. In addition, epidemiological evidence indicates that the prevalence of drinking and driving is higher at large (>10,000 student), public universities (Wechsler et al., 2003). Results of this study may not generalize to college settings with lower drinking and driving rates. The study is also limited by the use of self-report. However, self-report measures of substance-related behavior can be valid in youth, particularly when data collection is confidential or anonymous and when no consequences are associated with the report (Wilson & Grube, 1994).
An additional limitation of this study is that we did not include an assessment of quantity of marijuana use. Unlike alcohol, standardized self-report methods are generally not used to assess the amount of marijuana consumption. Therefore, although questions assessed driving or perceived danger of driving after different amounts of alcohol, parallel questions for marijuana did not specify an amount. This lack of specificity increases error variance due to individual differences in question interpretation. Future studies can use standardized interviews (Brown et al., 1998) to assess quantity of marijuana use and adapt these quantity measures to assess quantity of marijuana used prior to driving.
Results of this study also indicated significant overlap in youth who drive after use of alcohol and use of marijuana. Co-use of alcohol and marijuana prior to driving may be a particularly dangerous behavior, as co-use is associated with greater impairments in driving skills (Lamers & Ramaekers, 2001; Robbe, 1998). Future studies are required to examine youth cognitions and driving behavior associated with co-use of alcohol and marijuana.
The results of this study highlight cognitions about driving after use of marijuana as potential targets of prevention and intervention efforts. For drinking and driving, cognitive factors, such as perceived legal sanctions and normative beliefs, are associated with reduced drinking and driving in offenders receiving treatment (Greenberg et al., 2004). Drinking and driving offenders also cite legal sanctions as their primary motivation for avoiding drinking and driving (Wiliszowski, Murphy, Jones, & Lacey, 1996). Challenging youths' perceptions about the danger and potential negative consequences of driving after marijuana use may be an important technique for reducing this prevalent risk-taking behavior.
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Submitted: April 13, 2006 Revised: December 7, 2006 Accepted: December 8, 2006
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Source: Psychology of Addictive Behaviors. Vol. 21. (3), Sep, 2007 pp. 425-430)
Accession Number: 2007-13102-018
Digital Object Identifier: 10.1037/0893-164X.21.3.425
Record: 25- Gambling-Related Cognition Scale (GRCS): Are skills-based games at a disadvantage? Lévesque, David; Sévigny, Serge; Giroux, Isabelle; Jacques, Christian; Psychology of Addictive Behaviors, Vol 31(6), Sep, 2017 pp. 647-654. Publisher: American Psychological Association; [Journal Article] Abstract: The Gambling-Related Cognition Scale (GRCS; Raylu & Oei, 2004) was developed to evaluate gambling-related cognitive distortions for all types of gamblers, regardless of their gambling activities (poker, slot machine, etc.). It is therefore imperative to ascertain the validity of its interpretation across different types of gamblers; however, some skills-related items endorsed by players could be interpreted as a cognitive distortion despite the fact that they play skills-related games. Using an intergroup (168 poker players and 73 video lottery terminal [VLT] players) differential item functioning (DIF) analysis, this study examined the possible manifestation of item biases associated with the GRCS. DIF was analyzed with ordinal logistic regressions (OLRs) and Ramsay’s (1991) nonparametric kernel smoothing approach with TestGraf. Results show that half of the items display at least moderate DIF between groups and, depending on the type of analysis used, 3 to 7 items displayed large DIF. The 5 items with the most DIF were more significantly endorsed by poker players (uniform DIF) and were all related to skills, knowledge, learning, or probabilities. Poker players’ interpretations of some skills-related items may lead to an overestimation of their cognitive distortions due to their total score increased by measurement artifact. Findings indicate that the current structure of the GRCS contains potential biases to be considered when poker players are surveyed. The present study conveys new and important information on bias issues to ponder carefully before using and interpreting the GRCS and other similar wide-range instruments with poker players. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Gambling-Related Cognition Scale (GRCS): Are Skills-Based Games at a Disadvantage?
By: David Lévesque
École de Psychologie, Université Laval;
Serge Sévigny
Département des fondements et pratiques en éducation, Université Laval
Isabelle Giroux
École de Psychologie, Université Laval
Christian Jacques
École de Psychologie, Université Laval
Acknowledgement: The data and ideas in the manuscript were presented at the 11th European Conference on Gambling Studies and Policy Issues. David Lévesque was financially supported by the Fonds de recherche du Québec—Société et culture (FRQ-SC) and the Centre de réadaptation en dépendance de Montréal—Institut universitaire (CRDM-IU).
This study is based on the cross-sectional data that come from an unpublished doctoral thesis (Lévesque, 2017). Approval from the Université Laval Ethics Committee (Approval: 2013-038 A-2) was granted prior to commencement of the initial study in 2013.
Scientific research conducted over the last decades demonstrated the importance of the role of cognition in the conceptualization of gambling behavior. Cognition can be manifested as unique beliefs and attitudes toward control, luck, prediction, and chance (Ladouceur, & Walker, 1996; Langer, & Roth, 1975; Oei, Lin, & Raylu, 2008; Toneatto, 1999). Appropriate beliefs and attitudes are reflected in the perception that the game play and its outcomes are determined by chance; however, gamblers may develop the idea that results can be predicted and controlled (Barrault & Varescon, 2012). These cognitive distortions, also called irrational thoughts, appear to be related to the development and maintenance of problem gambling (Barrault & Varescon, 2012; Devynck, Giroux, & Jacques, 2012; Oei et al., 2008; Toneatto, 1999). Some studies suggest that they are more important for gamblers of skills-based games (Myrseth, Brunborg, & Eidem, 2010; Toneatto, Blitz-Miller, Calderwood, Dragonetti, & Tsanos, 1997).
Barrault (2012) used the GRCS (Raylu & Oei, 2004) to study cognitive distortions in 65 poker gamblers. She points out that some poker players could overestimate their skill level, which can be conceptualized as a cognitive distortion. However, she also observed that some cognitions may not be as erroneous as we think. The author reports Item 15 on the GRCS as an example (“Relating my losses to probability makes me continue gambling”) of a statement that may not be considered as a cognitive distortion among poker players in certain cases. When examining the GRCS items, one can try to identify which items could be an issue for strategic versus nonstrategic games (see Table 2 for a presentation of all items). At first sight, it seems possible that seven out of 23 items could be related to either skill (one’s skill or others’ skill: Items 5 and 22), probabilities (Items 10 and 15), or learning/experience (Items 9, 11, 14). Hence, these items could be more or less adapted to certain types of gamblers (e.g., poker players) and present measurement biases. Barrault (2012) emphasizes that at this time, no measure of cognitive distortions appear to be “totally adapted” to poker players. This view is shared by other researchers (Brochu, Sévigny, & Giroux, 2015; Devynck et al., 2012).
Item Means, Standard Deviations, and Results on Comparative Analyses on the Items According to Type of Game
Self-report measures of cognitive distortions usually play a role in screening all types of gamblers regardless of their gambling activities of choice. Accordingly, if the instruments used present biases according to the type of gambling game, there could be significant consequences. More notably, research findings using these instruments would be biased, and mislead clinicians who rely on these screening instruments. In short, for epidemiological, empirical, and clinical reasons, it is important to verify the potential cross-game bias of GRCS items.
This study’s main objective is to examine the DIF of the empirically validated French version of the GRCS (Raylu & Oei, 2004, French version: Grall-Bronnec et al., 2012) to shed light on one or more potential item biases relating to the type of gambling game. DIF is identified when a group responds differently to an item in comparison with a second group despite statistical control of the latent variable measured (e.g., total GRCS score). Accordingly, if a group endorses a GRCS item significantly more than the second group for a similar overall level of cognitive distortions (total GRCS score), the item is identified as presenting DIF. When DIF is detected, potential bias related to this item is suspected.
Essentially, the study seeks to answer the following research questions: (Q1) is DIF present among poker players and VLT players on the GRCS? (Q2) Do items presenting DIF have an impact on the measure? If the responses to some items vary between both groups, the presence of bias is assumed for these items. Items relating to the notion of skills may be more significantly endorsed by poker players as opposed to VLT players.
Method Participants
This study is based on the cross-sectional data of Lévesque’s unpublished doctoral thesis (Lévesque, 2017). The initial database included data from 272 gamblers. Participants had to meet the following inclusion criteria: (a) male, (b) at least 18 years of age, (c) gamble with money, (d) play poker at least twice a month or play VLTs at least once a month over the past 6 months, and (e) consider oneself as mainly a poker or VLT player. Participants who played both poker and VLT were excluded. Only participants who fully responded to the GRCS were retained for the current study. The final sample is comprised of 169 poker players and 73 VLT players. Participants are adults aged 19 to 82 years old (M = 35.29, Mdn = 29.00, SD = 15.57). Poker players are younger (M = 28.44, Mdn = 25.50, SD = 9.30) than VLT players (M = 51.07, Mdn = 54.00, SD = 15.66). Concerning poker players, 53.6% are students, 64.7% report having an annual income of $34,999 or less, 57.7% are single, and 82.2% have at least 11 years of education. As for VLT players, 43.8% work full-time, 54.8% report earning an annual income of $35,000 or more, 34.2% are single, and 68.5% have 11 years of education or less. The sociodemographic characteristics are similar to those found in studies conducted in the province of Québec, Canada (Dufour, Brunelle, & Roy, 2015; Sévigny et al., 2016) and in two populational studies (Young & Stevens, 2009; Svensson & Romild, 2014) recently conducted among similar groups of gamblers. The poker players (M = 2.07, SD = 2.57) had significantly lower scores for problem gambling than the VLT players (M = 6.61, SD = 6.28) (Welch’s t test = 5.99; p < .001).
Measurement Instrument
Cognitive distortions
The GRCS (Raylu & Oei, 2004) evaluates the presence, nature, and intensity of cognitive distortions among gamblers. This instrument includes 23 questions with 7-point Likert-type scale items (7 = strongly agree to 1 = strongly disagree). The higher the total score, the higher the number of gambling-related cognitions displayed. The measure has five subscales: (a) Perceived Inability to Stop Gambling (5 items; impaired control), (b) Interpretative Bias (4 items; reframing gambling outcomes), (c) Illusion of Control (4 items; ability to control gambling outcomes), (d) Gambling-Related Expectancies (4 items; expected effect of gambling) and (e) Predictive Control (6 items; ability to predict gambling outcomes). In the current study, the validated French version of the GRCS is used (Grall-Bronnec et al., 2012). The French version has good psychometric properties: acceptable indicators of the suitability of the factorial analysis (root-mean-square error of approximation = 0.07; confirmatory fit index = 0.93; normed fit index = 0.98; goodness of fit index = 0.88), good internal consistency of the subscales (Cronbach’s alpha > .7) and an adequate homogeneity coefficient (Loevinger’s H > 0.3). The five dimensions demonstrate good convergent and discriminant validity.
Problem gambling severity
The Problem Gambling Severity Index (PGSI; French version; Ferris & Wynne, 2001) is a validated self-report measure of problem gambling severity over the past 12 months. Nine items are evaluated with a Likert-type scale: never, sometimes, most of the time, and almost always. A score of 0 indicates nonproblem gambling while a score of 1 or 2 means low-risk gambling; a score of 3 to 7 designates moderate-risk gambling, and a score of 8 or more qualifies gambling as excessive (Ferris & Wynne, 2001). The PGSI presents good psychometric qualities (see Ferris & Wynne, 2001).
Procedure
Data for the original study was collected in two ways: online survey or telephone interview. The volunteer gamblers were recruited in the province of Quebec, Canada, via advertisements published in local and provincial newspapers, and e-mails sent through distribution lists at the Laval University. The advertisements were also published on social networks and discussion forums specifically for gambling-related issues. The recruitment campaign invited potential participants to complete the questionnaires on the LimeSurvey website or during a telephone interview. The telephone questions were presented in the same chronological order as the online version. Participants received compensation in the form of a gift certificate ($25 CAD) or participation in a drawing of five $100 CAD gift certificates. This procedure received approval from Laval University’s Research Ethics Committee (approval no.: 2013–038 A-2).
Statistical Analyses
The comparative analyses and verification of the undimensionality, and the OLRs were conducted using SPSS software (Version 21). The nonparametric kernel smoothing approach analyses were conducted using TestGraf software (Ramsay, 2000). In order to apply DIF analyses, unidimensionality of the measure was ascertained with Cronbach’s alpha, item-total correlations, and a principal components analysis (first and second factor ratio).
Comparative analyses
Preliminary comparative analyses (t tests) were conducted for each GRCS item to examine the relationship between the cognitive distortions and the preferred type of gambling game. Moreover, comparative analyses (analysis of variance [ANOVA]) were conducted between the two groups for total scores on the measure to verify if there was a significant difference between both groups.
DIF
Regarding DIF, the latent variable refers to the total GRCS score. Thus, pairing the participants of both gambling types with regard to their cognitive distortion level is necessary. When evaluating DIF, it is recommended to use several strategies and analyze the convergence of findings (Camilli & Shepard, 1994; Clauser & Mazor, 1998; Holland & Thayer, 1988). In this study, two strategies are used. The first method consists of using OLR (Zumbo, 1999) and the corresponding effect size measure. Within the scope of the present study, Zumbo’s (1999) threshold as well as Jodoin and Gierl’s (2001) more liberal threshold were used and compared.
The second method of DIF analysis consists of using nonparametric kernel smoothing analyses conducted with TestGraf (Ramsay, 2000). To detect DIF, the area between two item characteristic curves (intraclass correlation coefficient [ICC]) is calculated. If the β value of this area is superior to 0.30, it indicates the presence of a large DIF (Sachs, Law, & Chan, 2003; Santor, Ramsay, & Zuroff, 1994).
Examination of the impact of DIF on the measure
To verify the impact of problematic items and answer the second research question (Q2), comparative analyses using t tests for paired samples were conducted between the modified measure (created without the items presenting large DIF) and the original measure. Moreover, an intergroup comparative analysis (ANOVA) was conducted on participants’ total scores on the modified GRCS measure in order to verify whether results would be different from those obtained with the original measure.
ResultsThe results of factorial analyses conducted to verify the unidimensionality of the GRCS are presented in Table 1. All indicators suggest moderate support for the unidimensionality of the measure. Unidimensionality may be assumed when one dimension is clearly dominant (“general factor”) over other dimensions (Blais & Laurier, 1997).
Cronbach’s Alphas, Item-Total Correlations, and Results of the Principal Component Analysis on the GRCS
Preliminary comparative analyses provide evidence for significant differences for 11 of the 23 items of the GRCS. More precisely, poker players obtain a significantly greater mean score than VLT players for Items 1, 5, 9, 11, 15, and 22. On the contrary, VLT players present a significantly higher mean score than poker players for Items 7, 12, 17, 19, and 23. As for the instrument’s total score, poker players (M = 58.35, SD = 19.75) do not significantly differ from VLT players (M = 56.04, SD = 24.86; Welch’s F(1, 113.24) = 0.49; p = .484). Results of the comparative analyses are presented in Table 2.
Results of the DIF analyses are similar for OLR and the kernel smoothing estimation (TestGraf; Table 3). Items 5, 9, and 15 are identified as presenting DIF of high importance according to Zumbo’s (1999) criteria. When using Jodoin and Gierl’s (2001) criteria, the number of items presenting large DIF increases as they are less conservative than Zumbo’s (1999) criteria. Items 5, 7, 9, 13, 15, 22, and 23 present large DIFs, whereas Items 4, 11, 12, 17, 18, and 19 present moderate DIFs. As for the kernel smoothing estimation analyses (TestGraf), they identify five items with large DIFs (β > 0.30): Items 5, 9, 11, 15, and 22. Convergence of the results points to three items (5, 9, and 15) presenting large DIFs, all of uniform predominance and for which potential bias favors their endorsement by poker players. The ICC for Items 5, 9, and 15 for both subgroups are presented in Figure 1.
DIF Analyses of GRCS Items Between Poker and VLT Players
Figure 1. Plots showing differential item functioning (DIF) of Items 5, 9, and 15 for poker players (Line 1) and video lottery terminal (VLT) players (Line 2). Note: For each plot, the horizontal axis is the score or maximum likelihood estimates of the latent trait, and these scores are related to the total scores of the Gambling-Related Cognition Scale. The vertical axis is the degree of endorsement of the items. The dashed lines indicate the percentage of respondents that fell below various latent trait scores. The line numbered “1” is the mean score for poker players, whereas the line numbered “2” is the mean score for VLT players. DIF is inferred from the area formed between these lines, with larger areas indicating more DIF. The composite DIF is a weighted function of the difference between the two lines across the different trait levels. Composite DIF values >.30 are inferred as demonstrating large DIF.
To verify the impact of potentially biased items on the GRCS, the first comparative analysis reveals that the mean total score is significantly lower (M = 2.54, SD = 0.86 vs. M = 2.31, SD = 0.88) when Items 5, 9, and 15 are withdrawn for poker players: t(167) = 16.95, p < .001, and statistically higher (M = 2.44, SD = 1.08 vs. M = 2.49, SD = 1.09) for VLT players: t(72) = −3.46, p = .001. The ANOVA conducted on the total scores of the modified measure do not show any significant intergroup differences (p = .234), as was the case with the original measure. Two final post hoc comparative analyses were then conducted to explore the potential impact of the three items of interest on their subscales of origin (Interpretative Bias: Items 5 and 15; and Predictive Control: Item 9). In the original instrument, poker players presented a higher score on the Interpretative Bias subscale than VLT players (F(1, 238) = 24.45, p < .001). Yet, this difference disappears once Items 5 and 15 are removed from the subscale (F(1, 240) = 0.109, p = .741). No difference was found between the groups for the original instrument or the modified instrument with regard to the Predictive Control subscale.
Validity check
Invariance of the results was tested to avoid a potential confounding measurement bias with between-groups differences. Thus, DIF analyses (OLR) were conducted on three items (5, 9, and 15) with gamblers who have low problem gambling severity or no problem (PGSI <2) only. The results are similar (large DIF; ΔR2 > .108, p < .01.) to those obtained with the original sample. Correlation analyses were also conducted to examine the possible confounding effects of age, education, income, and gambling problem severity when interpreting the DIF results. As seen in Table 4, these variables cannot be considered as confounding variables in the present sample because of the absence of correlation between them and the three items of interest. Only one correlation was possibly problematic (.228 between education and Item 15 for medium GRCS scores: R2 = .05). All positive correlations between problem gambling severity and items (see Table 4) support our subsequent interpretation of the results (see the Discussion section). For example, the .330 correlation between problem gambling severity and Item 15 scores means that the more severe the poker player’s gambling problem, the more they will endorse Item 15. Considering that poker players in the total sample were less problematic than VLT players, Item 15 probably would have been more highly endorsed if the sample was comprised of more problematic poker gamblers. Thus, the difference between VLT and poker players on that item would have been greater than currently reported.
Bivariate Spearman Correlations Between Sociodemographic Variables; Problem Gambling; Items 5, 9, and 15 for Low, Medium, and High Total GRCS Scores Across Poker and VLT Players
DiscussionThe GRCS is an instrument created for all gamblers and validated among groups playing different types of games: games of pure chance and games involving some skill (Grall-Bronnec et al., 2012; Raylu & Oei, 2004). Given that it is intended for all types of gamblers, the administration and interpretation of the instrument should be uniform for all gamblers. This study aimed to assess GRCS items (French version) in order to shed light on potential biases relating to gambling activity type. The results show that several items present moderate or large DIF. The themes relating to Items 5, 9, and 15 (large DIF) are skills, probabilities, and abilities or learning related to gambling behavior. Poker players endorse many more of these items than VLT players, regardless of their total GRCS score. This could be explained in part by poker players’ perception of these items, which may differ due to the portion of skills inherent to poker regardless of the degree of their cognitive distortions and problem gambling severity. In the end, this group’s total score is “inflated” because of a methodological artifact, which would be interpreted as a greater intensity of cognitive distortion. Hence, a question that arises is whether these items are indeed referring to actual cognitive distortions. In this regard, Young and Stevens are emphatic: it would be false to believe that the notion of skill is the product of a simple erroneous belief (Stevens & Young, 2010; Young & Stevens, 2009). According to these authors, the notion of skill is a structural characteristic specific to a distinct category of gambling games and not a belief manifested independently of the facts (Stevens & Young, 2010; Young & Stevens, 2009). Considering the findings of this study as well as Young and Stevens’ arguments, one question arises: is it conceptually appropriate to include items pertaining to the notion of skill within a measure of cognitive distortions targeting players of skill-related gambling games? This question is not easy to answer as it inevitably refers to the definition and reliable evaluation of cognitive distortion. Langer (1975) defines cognitive distortion as “an expectancy of a personal success probability that is higher than the objective probability should warrant” (p. 313). However, in poker, the objective probability of success depends on several factors (cards dealt, game experience, etc.), rendering its calculation complex (Linnet et al., 2012; Linnet, Gebauer, Shaffer, Mouridsen, & Møller 2010, Siler, 2010). Because the inherent portion of skill in poker is real but the objective probability of success is immeasurable, self-evaluation of one’s skills is not exempt from errors, and the GRCS items as currently formulated do not make it possible to determine the true amount of cognitive distortion in a player’s evaluation. Therefore, more attention should be directed toward the structural composition of the type of gambling game to identify a player’s cognitive distortions, and toward the subsequent development of items to evaluate this concept.
This study also examined the impact of the GRCS’ potentially biased items by using the measure after having withdrawn the items identified as presenting large DIF. The modified measure did not change the absence of a significant difference in total scores between the two groups (poker vs. VLT); however, for one same group, a significant difference was observed between the means for the original measure and the modified measure. Moreover, post hoc comparative items revealed the larger impact on the Interpretative Bias subscale. This subscale seems more or less adapted to poker players. Studies evaluating cognitive distortions should consider the preferred type of gambling game when analyzing their data.
Now, what should be done with items presenting DIF in the instrument’s current form? According to Zumbo (1999), simply eliminating these items would limit the factor of interest or the concept measured. The author also points out that an item presenting DIF does not necessarily mean that it is problematic, just as an item without differential functioning may present an undetected bias. Further studies on the measure are therefore recommended. In the present study, items identified as potentially biased should be subject to critical analyses by experts and theoreticians of the cognitive approach to better understand the causes of this differential functioning. We focused on three largely biased items (to be conservative) to show that poker players are disadvantaged by some items related to skills. The main problem with biased items is that they lead to an invalid interpretation of the GRCS observed scores. If the GRCS were to measure gambling-related cognitions in order to compare two or more groups, these groups should not differ based on the type of game played (skills vs. no skill); otherwise, these differences may be due to item bias rather than real group differences on the construct being measured. Of course, the GRCS may possess other biases that were not examined here; these could be examined in other studies. It is necessary to continue research on how to correctly measure erroneous thinking among poker players and, on a larger scale, among players of gambling games involving skill.
Some limitations should be considered when interpreting the findings of this study. Sample size in DIF analyses have an effect on item detection (Acar, 2011). Small samples like those of the present study could underestimate the number of DIF items. In order to increase the findings’ generalizability, this study must be replicated with larger and more representative samples (e.g., samples comprising players of both genders). Second, even though the GRCS may resemble other measures that evaluate cognitive distortions, it is not identical to them. Thus, the findings may not be generalizable to other measures.
This study is the first to apply a DIF analysis to a measure of cognitive distortions in gambling. The findings suggest that a preferred type of gambling game may influence gamblers’ response patterns on the GRCS. The issue of DIF is important as it refers to the notion of equity between groups: a measure of cognitive distortions should not potentially advantage or disadvantage a group. Finally, this study leads to new research questions, namely, regarding the “cross-game” biases of “one size fits all” measures. In this context, the development of new scales specific to certain types of gambling games could also be an avenue for further studies. In conclusion, clinicians and researchers should be careful when using and interpreting the GRCS measure. Special attention should be paid to the items identified as manifesting DIF in this study.
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Submitted: December 19, 2016 Revised: May 15, 2017 Accepted: May 17, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (6), Sep, 2017 pp. 647-654)
Accession Number: 2017-30690-001
Digital Object Identifier: 10.1037/adb0000297
Record: 26- Higher stimulus control is associated with less cigarette intake in daily smokers. Ferguson, Stuart G.; Shiffman, Saul; Dunbar, Michael; Schüz, Natalie; Psychology of Addictive Behaviors, Vol 30(2), Mar, 2016 pp. 229-237. Publisher: American Psychological Association; [Journal Article] Abstract: It is well established that environmental stimuli influence smoking in light, and to a lesser degree, heavy smokers. A 2-factor model of dependence suggests that the influence of stimulus control is masked among heavier smokers who primarily smoke for nicotine maintenance. The current study aimed to assess the influence of stimulus control across a range of moderate to heavy daily smokers. Furthermore, as local tobacco control policies may change the role of stimulus control, the study aimed to replicate previous U.S. findings on stimulus control in an Australian setting marked by strong tobacco control policies. In 2 Ecological Momentary Assessment studies, 420 participants monitored antecedents of smoking and nonsmoking situations. In a set of idiographic logistic regression analyses, situational antecedents were used to predict smoking occasions within each individual’s data. Linear regression analysis was used to test for the association between stimulus control and smoking rate, and to test for differences between the 2 samples. Daily smokers’ smoking was under considerable stimulus control, which was weaker at higher smoking rates. Overall, there was greater stimulus control in the Australian sample. Daily smokers also experience a degree of stimulus control, which is less influential in heavier smokers. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Record: 27- Identifying indicators of harmful and problem gambling in a Canadian sample through receiver operating characteristic analysis. Quilty, Lena C.; Avila Murati, Daniela; Bagby, R. Michael; Psychology of Addictive Behaviors, Vol 28(1), Mar, 2014 pp. 229-237. Publisher: American Psychological Association; [Journal Article] Abstract: Many gamblers would prefer to reduce gambling on their own rather than to adopt an abstinence approach within the context of a gambling treatment program. Yet responsible gambling guidelines lack quantifiable markers to guide gamblers in wagering safely. To address these issues, the current investigation implemented receiver operating characteristic (ROC) analysis to identify behavioral indicators of harmful and problem gambling. Gambling involvement was assessed in 503 participants (275 psychiatric outpatients and 228 community gamblers) with the Canadian Problem Gambling Index. Overall gambling frequency, duration, and expenditure were able to distinguish harmful and problematic gambling at a moderate level. Indicators of harmful gambling were generated for engagement in specific gambling activities: frequency of tickets and casino; duration of bingo, casino, and investments; and expenditures on bingo, casino, sports betting, games of skill, and investments. Indicators of problem gambling were similarly produced for frequency of tickets and casino, and expenditures on bingo, casino, games of skill, and investments. Logistic regression analyses revealed that overall gambling frequency uniquely predicted the presence of harmful and problem gambling. Furthermore, frequency indicators for tickets and casino uniquely predicted the presence of both harmful and problem gambling. Together, these findings contribute to the development of an empirically based method enabling the minimization of harmful or problem gambling through self-control rather than abstinence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Identifying Indicators of Harmful and Problem Gambling in a Canadian Sample Through Receiver Operating Characteristic Analysis
By: Lena C. Quilty
Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada, and Department of Psychiatry, University of Toronto;
Daniela Avila Murati
Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada
R. Michael Bagby
Departments of Psychology and Psychiatry, University of Toronto, and Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada
Acknowledgement: This investigation was supported by an operating grant from the Ontario Problem Gambling Research Centre (OPGRC).
In 1985, an amendment to Canada’s Criminal Code granted Canadian provinces control over gambling and gaming devices (Korn, 2000). Since the 1990s, Canada has promoted its entertainment economy through legalized gambling, granting provincial governments increasing revenue without additional taxation (Campbell & Smith, 1998). Canada’s gambling industry expansion has provided opportunities for social and economic development, including job creation, tax reduction, debt repayment, and the funding of social programs (Azmier, Kelley, & Todosichuk, 2001). These broad societal benefits come at the cost of rising social and health consequences, however (Marshall & Wynne, 2003). The expansion of government-operated gambling venues, in conjunction with the development of new gaming technologies, has contributed to a significant increase in gambling participation among Canadians (Korn, 2000).
Researchers and policymakers strongly suggest that the proliferation of legalized gambling constitutes a public health issue due to the adverse effects that gambling participation can have on the prevalence of gambling pathology (Azmier et al., 2001; Volberg, 1994). Prior to Canada’s legalization of gambling, prevalence estimates of pathological gambling in Canada were less than 1%; rates increased to between 1.2% and 1.9% following this legislation (Ladouceur, 1996). Although some investigators have suggested that prevalence rates have remained consistent over time (Ladouceur, Chevalier, Sevigny, & Hamel, 2005; Stucki & Rihs-Middel, 2007), others have demonstrated an increase in pathological gambling (Shaffer & Hall, 2001). Further, a recent investigation taking measurement issues into careful consideration suggested that accessibility of gambling activities has been linked with gambling involvement and pathology (Sassen, Kraus, & Bühringer, 2011). Indeed, accessibility of gambling has been associated with gambling-related difficulties in several recent investigations (Moore, Thomas, Kyrios, Bates, & Meredyth, 2011; Thomas, Allen, Phillips, & Karantzas, 2011; Thomas, Bates, et al., 2011).
Despite current rates of gambling-related difficulties, pathological gambling treatment services remain underutilized (Toneatto et al., 2008). For many gamblers, such services—which often include abstinence as a therapeutic target—are the treatment of choice. According to Blaszczynski and Nower (2002), controlled gambling is a suboptimal treatment goal for some with pathological gambling. For example, pathological gamblers with co-occurring psychopathology may be less likely to benefit from moderation-focused treatments, as other conditions may negatively impact their gambling behavior (Blaszczynski & Nower, 2009). Further, empirical research suggests that gamblers presenting with more severe problems are more likely to eventually benefit from formal treatment and less likely to successfully initiate and achieve self-control (Hodgins & El-Guebaly, 2000).
Yet recovery from gambling pathology is possible through self-help methods rather than formal treatment (Hodgins & El-Guebaly, 2000). As Hodgins and El-Guebaly note, over 80% of gamblers endorsing difficulty report not wanting to seek formal treatment because they would rather try to cut down or stop gambling on their own. Indeed, gamblers with varied risk for, or severity of, pathology are likely to employ self-recovery strategies, such as limiting their frequency of play as well as the amount of money and time spent wagering (Wiebe, Mun, & Kauffman, 2005). Given the feasibility of the self-help approach among many gamblers, then, alternative pathways to recovery not necessitating professional assistance must be supported (Toneatto et al., 2008). Self-change or natural recovery from problematic gambling can be promoted by offering the general public information and education (Hodgins & El-Guebaly, 2000). It is critical that any such responsible gambling guidelines should be driven by empirical evidence (Blaszczynski, Ladouceur, & Shaffer, 2004). The derivation of quantitative behavioral indicators of harmful and problem gambling can support pathways to self-recovery and the maintenance of healthy gambling habits in this way.
Several researchers have undertaken this task, examining the relation between gambling involvement and gambling-related harm or pathology. Currie et al. (2006) evaluated the dose–response relation between gambling involvement and harm, that is, gambling-related problems of lesser severity than those associated with problem or pathological gambling. Using the national epidemiological investigation conducted by Statistics Canada, receiver operating characteristic (ROC) analyses generated optimal cutoffs including a gambling frequency of three times per month, and gambling expenditures of $1,000 CAN per year and of 1% of gross family income. Subsequently, Currie et al. (2008) conducted a replication study based on three provincial gambling surveys (i.e., Alberta, Ontario, British Columbia). Optimal cutoffs included a gambling frequency of once per week and expenditure of $85 CAN per month. Guidelines for gross income varied across provinces (1% in Ontario vs. 3% in Alberta). Based on the data collected in Alberta, the cutoff for gambling duration was 60 min per session. Finally, Currie, Miller, Hodgins, and Wang (2009) extended their previous work to include three different measures of harmful gambling, utilizing data culled from five gambling provincial surveys conducted in Alberta, British Columbia, Ontario, Manitoba, and Newfoundland. Optimal cutoffs included three times per month and 1% of gross income for gambling frequency and expenditures. Results were generally comparable regardless of the measure of harm employed. However, the cutoff for gambling expenditures varied from $153.50 CAN to $357 CAN per year from the most conservative measure of harm employed to the least conservative measure.
Independently, Weinstock, Ledgerwood, and Petry (2007) investigated the relation between posttreatment gambling behavior and harm in a sample of pathological gamblers. According to ROC analyses, the optimal indicators included a gambling frequency of once per month, duration of 1.5 hr per month, and expenditure of 1.9% of income per month. Subsequently, Weinstock, Whelan, and Meyers (2008) examined the behavioral indicators of problem gambling in a sample of college students. According to ROC analyses, problematic gambling cutoffs include a gambling frequency of 1.2 times per month, duration of 2.1 hr per month, and expenditure of 10.5% of monthly income.
Previous research thus suggests that quantitative indicators of harmful and problem gambling can be derived and may be comparable across distinct gambling populations. Such guidelines enable the development of evidence-based responsible gambling parameters to assist gamblers in the reduction of the intensity of their gambling behavior (Currie et al., 2008). The availability of this approach may encourage those experiencing gambling problems who do not wish to abstain from gambling to seek out problem-gambling treatment services (Ladouceur, 2005; Weinstock et al., 2007).
The Current InvestigationThe purpose of the current investigation study was to conduct a comprehensive examination of the relation between gambling involvement—as assessed by frequency of gambling behavior, amount of money spent on gambling activities, and time spent involved in games of chance—and harmful as well as problem gambling. We aimed to replicate the quantitative gambling cutoffs derived by Currie et al. (2006, 2008, 2009) and Weinstock et al. (2007, 2008). We further aimed to extend this work via the generation of cutoffs for overall gambling involvement as well as involvement in specific gambling activities (e.g., tickets, casino). We finally aimed to evaluate the unique contribution of related gambling activity indicators to predict harmful or problematic gambling, as such associations may highlight the most salient indicators in those engaging in multiple forms of gambling.
We utilized a combined clinical and community gambler sample to ensure a broad range of clinical and gambling features. We hypothesized that results for overall gambling involvement will replicate the quantitative behavioral indicators of harmful and problem gambling identified by Currie, Weinstock, and colleagues. Analyses of specific gambling activities were deemed exploratory; however, we hypothesized that the indicators associated with casino, bingo, and lottery play will be comparable with the gambling cutoffs derived for overall gambling involvement, as these forms of gaming exhibit the greatest prevalence in adult samples (Marshall & Wynne, 2003).
Method Participants
The community gambler sample consisted of 228 participants, including 115 men and 113 women ranging in age from 20 to 65 years (M = 40.47; SD = 13.12). Ninety participants met Diagnostic and Statistical Manual of Mental Disorders (DSM–IV; American Psychiatric Association, 1994) criteria for lifetime pathological gambling. Further, 38 participants exhibited other DSM–IV Axis I pathology (mood disorders, n = 16; anxiety disorders, n = 23; substance use disorders, n = 7; eating disorders, n = 1; n = 8 met criteria for more than one co-occurring disorder). The clinical sample consisted of 275 psychiatric outpatients, including 100 men and 175 women ranging in age from 18 to 65 years (M = 43.02 years; SD = 11.58). All participants met criteria for either a depressive disorder (n = 138; n = 119, major depressive disorder; n = 18, dysthymic disorder; n = 1, depressive disorder not otherwise specified) or a bipolar disorder (n = 137; n = 110, bipolar I disorder; n = 21, bipolar II disorder; n = 6, bipolar disorder not otherwise specified). Thirty participants met DSM–IV criteria for lifetime pathological gambling. Other co-occurring DSM–IV Axis I disorders were present in 137 participants (anxiety disorders, n = 108; substance use disorders, n = 39; eating disorders, n = 18; somatoform disorders, n = 10; adjustment disorders, n = 1; and impulse control disorders, n = 1; n = 56 met criteria for more than one co-occurring disorder).
Procedure
The community gambler sample was recruited via two advertising campaigns in local media for a study on personality, thinking patterns, and gambling behavior: one geared to those who have engaged in gambling activities (advertising text included, “Have you played the Slots or Bingo? Bought Scratch or Lottery Tickets? Have you bet on games of chance or skill?”) and one highlighting motivations for gambling and potential problematic gambling (advertising text included, “Do you gamble for fun? For money? Could you be gambling too much?”). Eligibility criteria included the presence of lifetime social or problem gambling, and being between 18 and 65 years of age, with at least 8 years of education and with proficiency in the English language. Exclusion criteria included having sought help for the treatment of gambling. A total of 443 individuals contacted the study and completed a telephone interview to determine eligibility. Of these, 309 people were deemed eligible for participation. Of these, 81 participants produced either invalid (n = 14) or incomplete (n = 67) assessment protocols. A final sample of 228 subjects completed the entire research protocol.
The clinical sample was recruited via local media advertisements for a study on “mood disorders and behavior.” Eligibility criteria included the presence a depressive, manic, hypomanic, and/or a mixed episode in the past 10 years, as assessed by the Structured Clinical Interview for DSM–IV (First, Spitzer, Gibbon, & Williams, 1995). Other criteria required for eligibility similarly included being between 18 and 65 years of age, having at least 8 years of education, and being fluent in English. Gambling involvement was not required for eligibility. Exclusion criteria included current severe mania, psychosis, and current substance intoxication, to ensure participants were able to tolerate the lengthy study protocol and/or to validly complete the study measures. A total of 610 individuals contacted and completed a telephone interview to determine eligibility. Of these, 300 people completed a 2-day assessment involving the administration of diagnostic interviews and self-report questionnaires to further determine eligibility. A final sample of 275 met all eligibility criteria and successfully complied with the research protocol.
Measures
The Structured Clinical Interview for DSM–IV (First et al., 1995) is a semistructured interview designed to assess Axis I disorders of the DSM–IV (American Psychiatric Association, 1994). This measure was utilized to confirm eligibility criteria for the clinical sample, and to provide a clinical characterization of both the clinical and community gambler samples.
The Canadian Problem Gambling Index (CPGI; Ferris & Wynne, 2001) is a multicomponent self-report questionnaire designed to assess gambling behavior as well as gambling-related harms in the general population. The CPGI has received rigorous psychometric testing prior to its incorporation in community surveys (Smith & Wynne, 2002). The CPGI enquires about frequency of play, duration of play per session, and monthly expenditures in the last year. Frequency is assessed with multiple-choice items, with the eight response alternatives ranging from “never” to “daily.” Duration is assessed in minutes, and expenditure is assessed in Canadian dollars; no ranges or limitations are placed on these two variables. The types of gambling activities assessed by the CPGI include tickets (i.e., lottery, daily lottery, sports lottery, instant win, scratch, raffle, fundraising), horse racing, bingo, casino (i.e., poker, blackjack, roulette, Keno, craps, Video Lottery Terminals (VLTs)), sports pools, cards or board games, games of skill (i.e., pool, bowling, darts), arcade or video games, Internet wagering, sports with a bookie, and investments (i.e., stocks, options, commodities market).
The CPGI generates the Problem Gambling Severity Index (PGSI), a nine-item index of gambling difficulties (Smith & Wynne, 2002). The PGSI measures harm through items that assess gambling behavioral problems (e.g., chasing losses) as well as gambling-related negative consequences (e.g., financial problems; see Currie et al., 2006). Moreover, each PGSI item is rated on a Likert-scale from 0 (never) to 3 (almost always).
Harmful and Problem Gambling
To assess the relation between level of gambling involvement and harm, several measures of harmful gambling were included. Although several instruments exist to assess problem and pathological gambling, no consensus exists regarding how to assess gambling-related harm. In order to be consistent with Currie et al. (2006), three measures of harm were calculated using items of the PGSI: (a) ≥2 PGSI items rated ≥1; (b) ≥2 PGSI items referring to negative consequences rated ≥1; and (c) ≥1 PGSI items referring to negative consequences rated ≥1. All analyses were conducted with each of these measures of harm, in turn, permitting the evaluation of the convergence between these closely related indices of gambling-related difficulty. Problem gambling was determined via a score of 8 or more on the PGSI (Smith & Wynne, 2002).
Statistical Analysis
ROC analysis was utilized to assess the classification performance of each gambling indicator and to select optimal cutoffs for harmful and problem gambling with respect to general gambling participation as well as specific gambling activities. ROC analysis determines the overall ability of a test to discriminate between two groups and the classification accuracy of every cut point associated with that test (Streiner & Cairney, 2007). More specifically, ROC analysis produces a graphical plot of the true positive rate (i.e., the number of participants correctly identified as gambling at a harmful or problematic level) against the false positive rate (i.e., the number of participants incorrectly identified as gambling at a harmful or problematic level) for each possible cutoff of a particular gambling indicator (i.e., frequency, duration, expenditure). This ROC curve effectively illustrates the inverse relation between sensitivity and specificity: Sensitivity measures the ability of a gambling indicator to accurately identify individuals with a condition, whereas specificity measures the ability of a gambling indicator to accurately identify individuals without a condition. The area under the curve (AUC) measures the overall classification performance of a gambling indicator: The further the curve from the 45-degree diagonal of the curve, the more accurate the test. AUC values thus range from 0 (100% misclassification) to 1 (100% correct classification), where .50 is representative of chance levels of correct classification. It is conventional for an AUC between .50 and .70 to be considered small, between .70 and .90 to be moderate, and over .90 to be high (Streiner & Cairney, 2007).
Consistent with previous research, cutoffs were identified by maximizing sensitivity and specificity. As participants reported engaging in more than one gambling activity and investing different amounts of time and money on diverse games of chance, a composite index was derived for overall gambling frequency, duration, and expenditures based on the highest value for each category. Due to the low rate of involvement in several specific gambling activities, analyses were restricted to those reported by a minimum of 50 participants.
A series of logistic regressions were undertaken to evaluate the unique contribution of indicators with groups of related indicators in the detection of the presence of harmful and problem gambling. Within these models, the χ2 value associated with the model provides information on the predictive utility as the group of indicators as a whole, whereas the χ2 value associated with each indicator provides information on the unique predictive utility of that variable. The odds ratio (OR) supplements the latter, providing information on the probabilities of risk corresponding to specific gambling indicators. Measures of harmful and problem gambling served as criterion variables in separate analyses. Indicators of harmful and problem gambling as identified in ROC analyses served as predictor variables.
Results Identifying Harmful and Problem Gambling Indicators
Overall gambling
AUC values, cutoff values, and associated sensitivity and specificity values for overall gambling and harmful and problem gambling are displayed in Table 1. An illustration of the curves associated with gambling frequency, duration, and expenditures and harmful wagering are displayed in Figure 1; ROC results, including sensitivity and specificity values associated with each cutoff value, are available upon request. All measures of harmful and problem gambling tested produced moderate prediction values. Moreover, all measures of harmful gambling generated similar cutoffs for gambling frequency, duration, and expenditure.
Results of Receiver Operating Characteristic Analyses Between Measures of Gambling Intensity and Indicators of Harmful and Problem Gambling
Figure 1. ROC curve indicating the performance of (a) gambling frequency, (b) gambling duration, and (c) gambling expenditures in classifying the presence of at least two negative consequences. The point on the curve farthest from the diagonal line is the cut point that maximizes sensitivity and 1-specificity.
AUC values for specific gambling activities and harmful and problem gambling are presented in Table 2, Table 3, and Table 4. Measures of harm and problem gambling tested generated small to moderate prediction values; only results at the moderate level are described in detail. Again, all measures of harmful gambling produced comparable cutoffs.
Results of Receiver Operating Characteristic Analyses Between Gambling Frequency and Indicators of Harmful and Problem Gambling for Specific Gambling Activities
Results of Receiver Operating Characteristic Analyses Between Gambling Duration Per Session and Indicators of Harmful and Problem Gambling for Specific Gambling Activities
Results of Receiver Operating Characteristic Analyses Between Gambling Expenditure per Month and Indicators of Harmful and Problem Gambling for Specific Gambling Activities
Specific gambling activities: frequency
For tickets, the gambling frequency cutoff associated with optimal sensitivity and specificity is once per month for harmful gambling (sensitivity = .67 to .69 and specificity = .73 to .76) and once per week for problem gambling (sensitivity = .51; specificity = .83). For wagering within a casino, the gambling frequency is “never” for harmful gambling (sensitivity = .68 to .72 and specificity = .76 to .78) and 5 times per year for problem gambling (sensitivity = .56; specificity = .87).
Specific gambling activities: duration
For bingo, the gambling duration cutoff is 2 hr 15 min per session for harmful gambling (sensitivity = .52; specificity = .82-.86). For casino play, the duration cutoff is 3 hr 10 min per session for harmful gambling (sensitivity = .47 and specificity = .84). For investments, the gambling duration cutoff is between 13 and 25 min per session for harmful gambling (sensitivity = .52-.54 and specificity = .86-.88).
Specific gambling activities: expenditures
For bingo, the gambling expenditure cutoff is $37.5 CAN per month for harmful gambling (sensitivity = .76; specificity = .62) and $95 CAN per month for problem gambling (sensitivity = .58; specificity = .83). For casino play, the gambling expenditure cutoff is $27.5 to $110 CAN per month for harmful gambling (sensitivity = .62 to .93; specificity = .48 to .74) and $180 CAN per month for problem gambling (sensitivity = .71; specificity = .65). For sports betting, the gambling expenditure cutoff is $42.5 to $65 CAN per month for harmful gambling (sensitivity = .52 to .66; specificity = .68 to .85). For games of skill, the gambling expenditure cutoff is $33.5 to $37.5 CAN per month for harmful gambling (sensitivity = .57 to .60; specificity = .80 to .82) and $33.5 CAN per month for problem gambling (sensitivity = .69; specificity = .64). For investments, the gambling expenditure cutoff is $7.5 CAN per month for harmful gambling (sensitivity = .55 to .61; specificity = .85 to .86) and $25 CAN per month for problem gambling (sensitivity = .63; specificity = .80).
Predicting Harmful and Problem Gambling
Logistic regressions results are displayed in Table 5. In Model 1A, gambling frequency, duration, and expenditures significantly predicted the presence versus absence of harmful gambling as a whole. Gambling frequency and duration were also significant, demonstrating that when considered together with frequency and duration, gambling expenditures do not contribute unique predictive value when predicting harmful gambling. ORs revealed that individuals gambling at a greater frequency are more likely to be classified as harmful gamblers; gambling duration may contribute little practical information in conjunction with gambling frequency. A similar pattern of results was found in Model 1B. Gambling frequency, duration, and expenditures predicted the presence versus absence of problem gambling as a whole. Of these indicators, gambling frequency and duration uniquely predicted problem gambling. Again, as individuals gamble more frequently, their likelihood of developing a gambling pathology increases. The OR associated with duration was again pragmatically close to zero.
Models Predicting Harmful and Problem Gambling Using General Gambling Involvement or Involvement in Specific Gambling Activities
In both Models 2A and 2B, the frequency of ticket and casino play predicted the presence versus absence of both harmful and problem gambling. Gambling frequency for both tickets and casino were significant, indicating that these gambling indicators predict harmful and problem gambling on their own. Although duration and expenditures associated with specific gambling activities predicted the presence versus absence of harmful and problem gambling as a whole, respectively, none of these predictor values contributed unique variance in the prediction of this outcome (Model 3A, Model 4B). On the other hand, the expenditures of (Model 4A) associated with specific gambling activities did not predict the presence versus absence of harmful gambling.
DiscussionThe current investigation evaluated the capacity of numerous indicators of gambling involvement to discriminate between the presence and absence of harmful and problem gambling. The application of ROC analysis to identify harmful gambling as well as problem gambling indicators revealed that numerous indices have moderate utility in this regard. The present study provides a replication and extension of previous work, including not only overall gambling involvement but also involvement in specific gambling activities. The results are reviewed next, with direct comparisons with previously supported cutoffs where possible.
Overall Gambling Involvement
For general gambling involvement, we derived a gambling frequency cutoff of once per month; the precise harmful gambling indicator supported in Weinstock et al. (2007). Currie et al. (2006, 2009) suggested a similar cutoff of two to three times per month. Currie et al. (2008) proposed a broader, but still comparable, cutoff of two to five times per month. For duration, results support a cutoff between 22.5 and 35 min. This cutoff diverges from the 60-min-per-session harmful indicator derived by Currie et al. (2008), and the 1 hr-30-min-per-session cutoff derived by Weinstock et al. (2007). For monthly gambling expenditures, results support a cutoff of $24.50 CAN. This cutoff again diverges from that of Currie et al. (2006, 2008). Currie et al. (2006) supported a monthly expenditure of $41.75 to $83.44 CAN, whereas Currie et al. (2008) introduced $33 to $85 CAN. Currie et al.’s (2009) subsequent investigation produced a gambling expenditure cutoff of $12.79 to $29.75 CAN per month, which is more comparable with the spending guideline proposed by this study.
The current study’s problem gambling indicators were comparable with several cutoffs supported by Weinstock et al. (2008). A discrepancy is evident between the present study’s frequency cutoff of once per week and Weinstock et al.’s corresponding cutoff of 1.2 times per month. It is possible that such contrast may be attributed to differences in sample: Weinstock et al. utilized a sample of college students, whereas the current study sample included both clinical outpatients and community gamblers. Yet this study’s problematic duration indicator of 1 hr 40 min per session is comparable with Weinstock et al.’s cutoff of 2 hr 6 min per month (or 2 hr 6 min per session in conjunction with frequency cutoffs).
Specific Gambling Activities
The implementation of ROC analysis to identify indicators of harmful and problem gambling behavior for specific gambling activities yielded a range of results. Guidelines were developed for games of chance with moderate levels of classification accuracy including: tickets, bingo, casino, sports betting, games of skill, and investments. The optimal cutoffs derived were particular to each gambling activity, and, for the most part, were not necessarily comparable with the harmful- or problem-gambling indicators produced in relation to overall gambling involvement or other specific gambling activities. The exception to this case was the harmful and problem-gambling frequency indicators for tickets, which were the precise frequency cutoffs presented by the current study with respect to overall gambling involvement. In addition, the problem-gambling expenditure indicator for bingo is also in agreement with the problem-gambling expenditure cutoff proposed by this study with respect to overall gambling involvement. It is notable that to be consistent with previous research, the cutoffs described herein are those associated with maximized sensitivity and specificity, which were often below preferred values. The choice of cutoff will be determined by the expected utility or consequences of true versus false test results (e.g., “misses” vs. “false positives”), and resulting emphasis on sensitivity versus specificity. Treatment contexts wishing to reduce the risk of relapse may be willing to tolerate a larger proportion of false positives, for example. These value judgments are important to consider in any application of such guidelines.
Logistic regression analyses revealed that although gambling frequency, duration, and expenditures, as a whole, effectively predict both the presence and absence of harmful and problem gambling, the unique prediction ability of overall gambling expenditures is not statistically significant, whereas that of overall gambling duration may be of questionable clinical significance. These results are striking in light of Currie et al.’s (2006) conclusion that what ultimately determines the impact of gambling is the gambler’s financial means. Logistic regression analyses further revealed that frequency indicators for tickets and casino individually predicted both the presence and absence of harmful and problem gambling. These results suggest that efforts to promote responsible gambling or curb problem gambling may focus upon activity-specific guidelines and upon some activities more than others.
The current study utilized the CPGI, an assessment instrument recognized for measuring gambling involvement indicators as well as problematic gambling. The CPGI not only inquires about gambling-related problems assessed by other well-known measures such as the South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987) but also evaluates gambling negative consequences (e.g., experiencing health problems; Stinchfield, 2002). One of the strengths of the CPGI lies in its capacity to ask for specific gambling expenditures or duration estimates, rather than providing ranges. The assessment of frequency is less exact, however. Duration and expenditures may provide useful or perhaps even more valid indicators. The cutoffs associated with frequency versus duration and expenditures for harmful casino play derived in the current investigation result in opposing recommendations, for example, and may be indicative of the need for more precise assays of these constructs. Further, confusion exists regarding how to rate activities that might fall in more than one category (e.g., online poker) in the CPGI, further emphasizing the need for measures sensitive to ever-evolving manifestation of gambling activities. Of note, cutoffs for each measure of harmful gambling derived from the PGSI were highly similar, suggesting that future work may be justified in utilizing only one such measure.
ConclusionsDeriving indicators of harmful and problem gambling through ROC analysis is a feasible approach. However, additional research of this nature is imperative. First, further research incorporating duration and expenditure is required, as the majority of the existing research has focused upon the frequency of gambling activities. Second, there is a dearth of research on the subject of identifying quantitative behavioral indicators of harmful or problem gambling with respect to specific gambling activities. Future research is crucial to replicate the results of this study and to further evaluate other activities that could not be fully evaluated by the current study, due to low involvement. Third, research incorporating alternate samples is necessary. Many of the optimal cutoffs generated by the current study were compared with the guidelines produced by other investigations based on clinical or college student samples. Research including representative community samples and adults across the life span (e.g., geriatric samples) would therefore be particularly beneficial. Indeed, the degree to which demographic and clinical characteristics moderate gambling guidelines requires empirical evaluation. The clinical significance of any differences in cutoffs across men versus women, for example, is of particular pragmatic use. Fourth, it is possible that some degree of underreporting might have taken place among certain participants. Although the conservative estimates that would result from an underreporting response still may be preferable, investigations incorporating measures of response bias would be of assistance.
The current study was limited by the few participants who engaged in the following activities: VLTs, arcade or video games, and Internet gambling. Due to an insufficient number of valid cases for these gambling activities, harmful- and problem-gambling indicators could not be produced for these activities. Furthermore, although the current investigation represents an important first step in this endeavor, it must be acknowledged that for some activity-specific ROC analyses, sample sizes were small. ROC results increase in accuracy with sample size, underscoring the need to conduct replications in large samples (Fluss, Faraggi, & Reiser, 2005).
Due to the absence of a conceptual rationale for selecting cutoffs, Currie et al. (2006, 2008, 2009) granted equal weighting to specificity and sensitivity, while maximizing the discrimination between the presence and absence of gambling harm. The current study employed the same approach. The confidence with which cutoffs can be applied is often evaluated via positive and negative predictive values (i.e., the probability of accurate classification), which are themselves influenced not only by sensitivity and specificity but also by the prevalence or baseline of gambling pathology in the sample evaluated. It is important to note, in this context, that positive and negative predictive values derived from the community gambler sample of the current investigation would thus be influenced by the elevated prevalence of gambling-related difficulties in the sample relative to the general population, due to our recruitment methodology and eligibility criteria. For example, in an at-risk sample such as ours—with a prevalence rate of pathological gambling of 39%—the probability that someone scoring above the cutoff actually experiencing gambling-related difficulties is .75. In contrast, in a representative community sample—with a prevalence rate of pathological gambling of 3% (Wiebe et al., 2005; Williams, Volberg, & Stevens, 2012)—the positive predictive value is only .13. False-positive rates clearly significantly impact the performance of a classification instrument within low base-rate samples. Thus, the sample in which these cutoffs will be used needs to be carefully considered in their utilization.
Generating safe gambling guidelines with respect to general gambling involvement and specific gambling activities may be not only feasible but also practical. Cutoffs of this nature could be integrated into diverse areas of work, including assessment, prevention, and treatment. Such indicators, for instance, could serve to enable the development of evidence-based responsible gambling guidelines. Advertising has widely promoted safe cutoffs for alcohol consumption among adult men and women; analogous campaigns highlighting excessively frequent casino visits, for example, would provide a clear message to the general population. Such parameters may be of considerable use to gamblers wishing to keep their gambling behavior under control. Treatment providers could employ these cutoffs to design treatments focused on self-control for gamblers whose addiction revolves around one or more than one gambling activity. Applications of such guidelines require rigorous empirical investigation prior to their widespread use in the prevention and treatment of gambling difficulties.
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Submitted: August 20, 2012 Revised: March 19, 2013 Accepted: March 25, 2013
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Record: 28- Identifying risky drinking patterns over the course of Saturday evenings: An event-level study. Kuntsche, Emmanuel; Otten, Roy; Labhart, Florian; Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015 pp. 744-752. Publisher: American Psychological Association; [Journal Article] Abstract: Gaining a better understanding of young adults’ excessive drinking on nights out is crucial to ensure prevention efforts are effectively targeted. This study aims to identify Saturdays with similar evening drinking patterns and corresponding situation-specific and person-specific determinants. Growth mixture modeling and multilevel logistic regressions were based on 3,084 questionnaires completed by 164 young adults on 514 evenings via the Internet-based cell phone optimized assessment technique (ICAT). The results showed that the 2-group solution best fitted the data with a 'stable low' drinking pattern (64.0% of all evenings, 0.2 drinks per hour on average, 1.5 drinks in total) and an 'accelerated' drinking pattern (36.0%, increased drinking pace from about 1 drink per hour before 8 p.m. to about 2 drinks per hour after 10 p.m.; 11.5 drinks in total). The presence of more same-sex friends (ORwomen = 1.29, 95% CI [1.09–1.53]; ORmen = 1.35, 95% CI [1.15–1.58], engaging in predrinking (ORwomen = 2.80, 95% CI [1.35–5.81]; ORmen = 3.78, 95% CI [1.67–8.55] and more time spent in drinking establishments among men (ORmen = 1.46, 95% CI [1.12–1.90] predicted accelerated drinking evenings. Accelerated drinking was also likely among women scoring high on coping motives at baseline (ORwomen = 2.40, 95% CI [1.43–4.03] and among men scoring high on enhancement motives (ORmen = 2.36, 95% CI [1.46–3.80]. To conclude, with a total evening consumption that is almost twice the threshold for binge drinking, the identified accelerated drinking pattern signifies a burden for individual and public health. Promoting personal goal setting and commitment, and reinforcing self-efficacy and resistance skills training appear to be promising strategies to impede the acceleration of drinking pace on Saturday evenings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Identifying Risky Drinking Patterns Over the Course of Saturday Evenings: An Event-Level Study
By: Emmanuel Kuntsche
Addiction Switzerland, Research Institute, Lausanne, Switzerland, and Behavioural Science Institute, Radboud University;
Roy Otten
Behavioural Science Institute, Radboud University
Florian Labhart
Addiction Switzerland, Research Institute, Lausanne, Switzerland
Acknowledgement: The study was supported by the Swiss National Science Foundation, Grant 100014_124568/1. We thank Valentin Vago for the development of the web-based application and Gemma Brown for English copyediting of this article.
The high prevalence of drinking in young adults is a serious public health concern, and risky drinking in young people is a primary cause of mortality and morbidity (Rehm, Gmel, Room, & Frick, 2001). However, it is not simply the fact that young people are drinking but the specific way in which they drink that puts them at such high risk of alcohol-related problems. Research consistently shows that people tend to drink the heaviest in their late teens and early to midtwenties (Gmel, Kuntsche, & Rehm, 2011; Kuntsche & Gmel, 2013).
Most young people drink heavily on weekends with a peak on Saturday evenings when people go out and do not have any work or study responsibilities the following day. Drinking at weekends tends to occur to such a frequent and excessive degree that authors in several countries have spoken of a “heavy drinking weekend culture” (Heeb, Gmel, Rehm, & Mohler-Kuo, 2008; Kuntsche & Cooper, 2010; Parker & Williams, 2003; van Wersch & Walker, 2009). This is worrisome since heavy drinking on evenings out is likely to result in a number of particularly detrimental consequences, such as accidents, injuries, victimization, and aggression (Graham & Wells, 2003; Harford, Wechsler, & Muthén, 2003; Nyaronga, Greenfield, & McDaniel, 2009; Rossow & Hauge, 2004).
In order to effectively target prevention efforts, it is crucial to better understand the patterns of young people’s drinking throughout the evening, particularly on Saturdays, which eventually leads to these consequences (Sunderland, Chalmers, McKetin, & Bright, 2014). However, despite extensive research in recent decades, little is known about what actually happens when young people are out, consuming alcohol in their “natural” environments. Using data collected by means of participants’ cell phones 6 times per evening, this study aims to identify evenings with similar drinking patterns (i.e., groups of evenings in which drinking from before 8 p.m. until after midnight happened in a similar way) and associated risk factors, which could contribute to more effective prevention strategies.
As little is known about what actually happens in practice, research about drinking patterns over the course of Saturday evenings is also scarce. A previous study showed that the average consumption at the beginning of the evening was about the same for Thursdays, Fridays, and Saturdays (Kuntsche & Labhart, 2012). However, over the course of the evening, consumption tended to decrease on Thursdays, remained stable over the course of the evening on Fridays, but increased on Saturdays. In other words, while participants drank on average less and less per hour on Thursdays, consumption remained stable at about 0.4 drinks per hour on average for women and 0.7 drinks per hour for men on Fridays. On Saturdays, however, consumption increased among men on average from fewer than 0.6 drinks per hour before 8 p.m. to more than 1.0 drink per hour after 10 p.m. (from 0.4 to 0.6 drinks for women). The authors therefore argued that it is important to focus prevention efforts on curbing the acceleration of young people’s drinking that is likely to occur on Saturday evenings (Kuntsche & Labhart, 2012).
It is still unknown whether this average increase in drinks per hour on Saturday evenings represents a specific evening drinking pattern. This is important, however, because in order to target prevention efforts more effectively, it is crucial to (a) identify evenings in which such an accelerated drinking pattern occurs and (b) to identify characteristics of evenings and individuals that predict whether or not accelerated drinking is likely to occur on a given evening. Previous studies have shown that heavy weekend drinking is likely to occur on evenings when friends are present, when people drink in a private location before going out (called predrinking, pregaming, or preloading), or when spending time in bars and among individuals that are male and drink for enhancement motives.
In this study, we collected data 6 times per evening covering the timeframe from 5 p.m. until 11 a.m. the next morning on 5 subsequent weekends using the recently developed Internet-based cell phone-optimized assessment technique (ICAT: Kuntsche & Labhart, 2013b). Using Growth Mixture Modeling (GMM), the first objective was to identify homogenous groups of Saturday evenings in which participants showed a similar progression of consumed drinks from 1 hr to the next throughout the course of the evening. We expect to find at least one homogenous group of evenings that is characterized by accelerated drinking, that is, in which an increasing number of drinks per hour are consumed.
The second objective was to use situation-specific and person-specific variables to predict the occurrence of accelerated drinking on a given evening. At situational level, we expect accelerated drinking when spending time in bars or with friends (Clapp & Shillington, 2001; Harford, Wechsler, & Seibring, 2002; Hartzler & Fromme, 2003; Wilks & Callan, 1990). At the individual level, we expect accelerated drinking to be more likely among men than among women and among those who score high on enhancement motives.
Method Study Design and Sample
Using ICAT (Kuntsche & Labhart, 2013b), data were collected between April and July 2010 by means of a baseline questionnaire to be completed online directly after registration and a series of Internet-based questionnaires that participants completed on their personal cell phones. In Lausanne and Geneva, the two major cities in French-speaking Switzerland, participants were recruited from three higher education institutions. At each institution, an e-mail containing information about the study with a hyperlink to the study’s homepage was submitted to all students. Both the e-mail and the study homepage provided information on the aim of the study, explained that any answers were voluntary and would be treated as confidential, and outlined the participation incentives (i.e., those who returned at least 80% of the cell phone questionnaires would receive a randomly drawn voucher worth 40 to 80 USD). The homepage also provided a hyperlink, which allowed participants to test whether their cell phone enabled mobile Internet access. To register, participants had to indicate their cell phone number at the bottom of the homepage. The study was approved by the Ethical Committee of Lausanne University (protocol no. 223/08).
On Thursdays, Fridays, and Saturdays on 5 subsequent weekends, text messages (SMS) containing unique hyperlinks were sent to the participants’ cell phones at 8 p.m., 9 p.m., 10 p.m., 11 p.m., midnight, and the next morning at 11 a.m. Clicking on the hyperlink automatically opened a blank questionnaire in the cell phone browser. Different time frames were used to be able to cover the entire evening with a reasonable response burden, that is, the questionnaire at 8 p.m. referred to the events between 5 p.m. and 8 p.m.; the following questionnaires (i.e., at 9 p.m., 10 p.m., 11 p.m., and midnight) referred to the preceding hour; the questionnaire at 11 a.m. referred to the events since midnight. To minimize recall bias, questionnaires could only be accessed once and only within the 12-hr period following dispatch of the SMS.
The overall analytic sample consisted of 7,828 assessments on 1,441 evenings provided by 183 participants. Since excessive drinking is particularly likely to occur on Saturdays (Kuntsche & Labhart, 2012), only the 514 Saturday evenings, based on 3,084 single questionnaires reported by 164 participants (54.3% females), were included in this study.
Measures
Individual-level characteristics included in the baseline Internet questionnaire
Participants were asked to indicate whether they were female (coded as 0) or male (1) and their year of birth. The 20-item Drinking Motive Questionnaire Revised (DMQ-R; Cooper, 1994) was used to assess four conceptually and empirically distinct dimensions of drinking motives: enhancement motives (e.g., drinking to have fun and to get drunk), social motives (e.g., to improve parties and to celebrate a special occasion with friends), coping motives (e.g., to forget about worries and problems), and conformity motives (e.g., to fit in with a group and not to feel left out). Participants were asked to consider all the times they had drunk alcohol in the last 12 months and to indicate, on a relative frequency scale ranging from never/almost never (coded as 1) to almost always (coded as 5), on how many occasions they had drunk for each given motive. Internal consistencies were αsocial = .80, αenhancement = .72, αcoping = .74, and αconformity = .56. For each of the four dimensions, the five items were averaged.
Evening-level characteristics included in the cell phone questionnaires
To assess alcohol use in the evenings, the question was “How many of the following alcoholic drinks did you have between . . .?” At baseline, participants were shown pictograms of “standard drinks,” corresponding to 10 g of pure alcohol (Kuntsche & Labhart, 2012). The time frames of the 6 evening assessments were 5–8 p.m., 8–9 p.m., 9–10 p.m., 10–11 p.m., 11 p.m.–midnight, and since midnight (assessed at 11 a.m. the next morning). With separate questions, participants could indicate how many beer, wine, champagne, aperitifs (e.g., port), liqueur at 20°, (straight) spirits, self-mixed drinks (e.g., whisky-coke), cocktails, and alcopops they had consumed in the given time frame. Answer categories were 0, 1, 2, 3, 4, and 5 or more (coded as 5.5). Since the first and the last assessment referred to an extended time period (i.e., 5–8 p.m. and after midnight, respectively), as recommended by Kuntsche and Labhart (2012), two thirds of the indicated consumption was taken to approximate the consumption shortly before 8 p.m. and shortly after midnight.
To assess the number of male and female friends present, the question was “How many people were you with between . . .?” The time frames were the same as the first 5 for alcohol use mentioned above. Two questions asked participants to indicate how many male and female friends (including romantic partner) were present in the given time frame. Answer categories were 0, 1, 2–4, 5–20, and 20 or more. Midpoints of categories were used, and 23.5 for the upper category (20 persons plus half range to midpoint of adjacent category, i.e., 3.75 = 20 – ((12.5 + 20)/2)), to create a linear measure that represents the actual number of friends present (Labhart, Wells, Graham, & Kuntsche, 2014). For each evening, the 5 assessments were averaged.
Participants were also asked how much time they spent at different locations (at home, traveling, etc.) at 5–8 p.m., 8–9 p.m., 9–10 p.m., 10–11 p.m., and 11 p.m.–midnight. One question asked about the time spent in bars and other drinking establishments (restaurants, pubs, nightclubs, etc.). Answer categories were given in half-hour increments (0, 30, 60, up to 180 min) for the 5–8 p.m. assessment and in quarter-hour increments (0, 15, up to 60 min) for the 4 subsequent 1-hr assessments.
Predrinking was defined as the consumption of at least 1 drink off-premise (e.g., at home or at a friend’s home, while traveling, outdoors) before spending time in an on-premise establishment (e.g., restaurants, pubs or nightclubs, cultural or sporting venues; Labhart, Graham, Wells, & Kuntsche, 2013). The variable was coded 1 if this happened at a given evening and 0 otherwise.
Alcohol-related consequences were mostly taken from the Brief Version of the Young Adult Alcohol Consequences Questionnaire (B-YAACQ; Kahler, Strong, & Read, 2005) and included in the 11 a.m. questionnaire. Participants were asked: “Did any of the following occur last night as a result of your drinking?” Response categories were: hangover, drunk driving, fight or quarrel, injured self or someone else, blackout (not remembering what happened even for a short period of time), unplanned use of other substances, unintended or unprotected sexual intercourse, and property damage or vandalism. Because of severe space constraints in the cell phone questionnaire, we selected only concrete outcomes that were likely to be experienced (Labhart et al., 2013). The 8 consequences (coded as 0 = no; 1 = yes) were added up on a scale ranging from 0 to 8.
Statistical Analyses
Analyses in this study were conducted in three steps. In the first step, we used GMM, a semiparametric clustering technique (Muthén & Muthén, 1998–2012), to identify homogenous groups of evenings based on similarities in alcohol consumption at the beginning of the evening (intercept) and the progression of drinking pace (number of drinks consumed per hour) throughout the course of the evening (slopes). To determine the number of groups that would best fit the data, a series of models with an increasing number of groups was fitted starting with a one-group model, moving to a six-group model. For simplicity and robustness reasons, this was first done based on a linear slope (assuming a strictly linear increase in the number of drinks consumed per hour). However, since a previous publication (Kuntsche & Labhart, 2012) indicated that the increase in drinking pace stabilized later in the evening, we subsequently included an additional quadratic slope in the two best-fitting models.
Evaluation of the best-fitting model was accomplished using the Bayesian Information Criterion (BIC), Akaike Information Criteria (AIC), the entropy, and the Lo-Mendell-Rubin Adjusted LRT Test (LMR). The BIC and the AIC are commonly used fit indices where lower values indicate a more parsimonious model (Akaike, 1987; Raftery, 1995). Entropy is a measure of classification accuracy where values closer to one indicate greater precision (McLachlan & Peel, 2000). Finally, the LMR test compares the improvement in fit between neighboring models. (i.e., comparing k - and the k-g -class models, where g < k) and provides a p value that can be used to determine if there is a statistically significant improvement in fit for the inclusion of one more class (Lo, Mendell, & Rubin, 2001). Because we assessed the number of drinks throughout the evening, we treated the dependent variable as count data. Count data can take only nonnegative integer values that arise from counting (e.g., Cameron & Trivedi, 1998). Obviously, in this study, the counts represent the number of drinks within a certain period of time.
In a second step, after determining the number of groups that best fitted the data, posterior probabilities were used to assign the evenings to one of the groups that were subsequently statistically compared in terms of group size, individuals’ mean age, gender proportion, total drinks in the evening, and resulting consequences. Because of the clustering of evening observations within individuals, differences between the groups of evenings were tested in the software STATA SE 12.0 (StataCorp, 2011) by using design-adjusted proportion and mean tests.
In a third and final step, a multilevel logistic regression model was estimated to predict membership in the identified groups of evening drinking patterns. The group with the lowest overall consumption was taken as reference group and membership in the other emerging groups was regressed (a) at evening level, on the number of male and female friends present, whether predrinking happened that evening or not, and the time spent in bars and other drinking establishments and, (b) at individual level, on age, and the four drinking motive dimensions. Because of known gender differences in weekend evening drinking, the models were estimated for males and females separately using the software Mplus 7 (Muthén & Muthén, 1998–2012).
Results Sample Description
On average, three male and two female friends were present during the five Saturday evenings included in the study (see Table 1). On one in six evenings, participants drank before going out (predrinking). On more than one third of the Saturday evenings, participants spent time in a bar or another drinking establishment and if they did, they stayed there for 2 hr and 20 min on an average evening. Over the course of the evening, males consumed more than two drinks more than females (6.3 vs. 4.0) on average. Males also reported experiencing more consequences (on average on 2 of the 5 Saturdays that were included in the study, whereas for women this was only 1). There was no age difference between males and females. Males scored higher on social motives than females, but no differences were found for the other motive dimensions.
Sample Description Means and (Standard Deviations)
Identifying and Describing Evening Drinking Patterns
The results of the GMM procedure were in favor of either a two-group or a three-group solution (upper part of Table 2). The entropy was superior for a two-group solution (0.872), indicating a particularly accurate classification. Moreover, the LMR p value of 0.000 illustrated that a two-group model would better fit the data than a one-group model. Solutions with more than two groups were less accurate, as demonstrated by lower entropy values. However, the three-group solution showed a significant p value for the LMR, together with lower AIC, BIC, and χ2 values indicating a slightly better fit to the data. Similar results were found when performing the GMM separately by gender.
Model Fit of the GMM Estimated
For the two-group and the three-group solutions, models with an additional quadratic slope were estimated (lower part of Table 2). The results showed that the entropy was identical for the two-group solution with or without quadratic trend. However, the slightly lower AIC, BIC, and χ2 values for the former than for the latter indicated a slightly better fit of the two-group model with a quadratic trend. For the three-group solution, the entropy was basically the same, the AIC and BIC values were slightly lower, but the χ2 value was higher in the model with quadratic slope than in the one without. Moreover, among the models with quadratic slope, the three-group model was not better fitted to the data than the two-group model as expressed by a nonsignificant LMR value. Taking the results together and also considering the parsimonious principle, it appears that the two-group model with a linear and a quadratic trend provided the best representation of the data.
Almost two thirds of the evenings (64.0%, Table 3) were characterized by a stable low drinking pattern (see Figure 1), that is, the slight decrease from 8 p.m. until midnight was not statistically significant, Blinear slope = −0.133, SE = 0.149, p > .05. There was also no change in the kind of progression throughout the evening in terms of a quadratic trend, Bquadratic slope = 0.004, SE = 0.034, p > .05. More than one third of the evenings (36.0%) were characterized by accelerated drinking, BSlope = 0.354, SE = 0.063, p < .001, with a consumption of almost 1 drink per hour on average at the beginning of the evening increasing to a consumption of almost 2 drinks an hour on average. From 10 p.m. on, acceleration ceased (expressed by a significant quadratic trend: Bquadratic slope = −0.046, SE = 0.011, p < .001), that is, drinking pace was more constant with an average of 2 drinks per hour.
Statistical Description of the Two Drinking Trajectories Emerging From the GMM
Figure 1. Graphical representation of the two drinking trajectories emerging from the growth mixture model based on the raw data, split by gender. Since the first and the last assessment referred to an extended time period (i.e., 5–8 p.m. and later than midnight, respectively), two thirds of the indicated consumption was taken to approximate the consumption shortly before 8 p.m. and shortly after midnight (Kuntsche & Labhart, 2012).
Figure 1 also shows that the two evening drinking patterns were similar for males and females. Adding gender as a predictor to the GMM revealed no gender differences in the shape of the evening drinking pattern in both the stable low (Blinear slope difference for men = 0.166, SE = 0.315, p > .05; Bquadratic slope difference for men = 0.002, SE = 0.066, p > .05) and the accelerated pattern (Blinear slope difference for men = 0.003, SE = 0.129, p > .05; Bquadratic slope difference for men = −0.001, SE = 0.002, p > .05). The proportion of males in the accelerated drinking pattern was higher than in the stable low pattern (see Table 3), however. There were no differences in mean age. During accelerated drinking evenings, participants consumed an average of around 8 times more alcoholic drinks and reported more than 17 times more adverse alcohol-related consequences as on evenings classified as stable low.
Predicting Evening Drinking Patterns
The higher the number of same-sex friends present, engaging in predrinking, and the more time spent in bars and other drinking establishments (significant for men only), the higher the likelihood that accelerated drinking occured on a given evening (see Table 4). However, a higher number of female friends present decreased the likelihood of accelerated drinking among males. Females who scored high on coping motives and low on conformity motives at baseline had a higher likelihood of engaging in accelerated drinking over the five Saturday evenings included in the study. The same was true for males in respect to enhancement motives.
ORs (95% CI in Brackets) of the Multilevel Logistic Regression Model Predicting Occurrence of the Accelerated Evening Drinking Pattern
DiscussionThis study aimed to identify Saturday evenings with similar patterns of alcohol consumption. GMM resulted in two groups characterized by a stable low and an accelerated evening drinking pattern. It is known that excessive drinking is likely to occur on Saturday evenings when people go out and do not have any work or study responsibilities the following day (Gmel, Gaume, Faouzi, Kulling, & Daeppen, 2008; Heeb et al., 2008; Kauer, Reid, Sanci, & Patton, 2009; Kuntsche & Labhart, 2012; Parker & Williams, 2003). The present study adds two important issues to this literature. First, excessive drinking did not occur in the majority of Saturday evenings. In 65% of the evenings, consumption remained low with one and a half drinks on average consumed during the entire evening. Second, if excessive drinking occurred, the consumption was characterized by accelerated drinking pace with an increasing number of drinks consumed per hour until around 10 p.m. and a stabilization at around two drinks per hour afterward. This was the case in about one in three Saturday evenings, and the total consumption was more than 11 drinks on average, which is actually more than twice the threshold for binge drinking (i.e., four or more drinks for women and five or more drinks for men; Kuntsche & Labhart, 2013a; Wechsler & Nelson, 2001). Losing self-control with increasing inebriation, the personal intention to get drunk, social pressure, or drinking norms have been shown to fuel excessive drinking and may be among the driving factors for consuming more and more drinks per hour.
This is worrisome since heavy drinking on evenings out is likely to result in a number of particularly detrimental consequences, such as accidents, injuries, victimization, and aggression (Graham & Wells, 2003; Harford et al., 2003; Nyaronga et al., 2009; Rossow & Hauge, 2004). In this study, we found 17 times more alcohol-related consequences following evenings with accelerated drinking than after stable low drinking evenings (see Table 3).
To inform and better target prevention strategies, this study revealed evening-specific and individual-specific factors that discriminated between stable low and accelerated drinking evenings. The latter, drinking at an accelerated pace, was more likely when same-sex friends were present during the evening. Among males, masculine drinking norms, a lack of self-regulation, and peer pressure appear likely explanations (Borsari & Carey, 2001; Iwamoto, Cheng, Lee, Takamatsu, & Gordon, 2011). In females, drinking is usually more affected by personal anticipation related to the consequences of drinking (Suls & Green, 2003), and females specifically report drinking in the company of close friends who they trust to safeguard their well-being as an important factor when getting drunk (Sheard, 2011). For males, the presence of women decreased the likelihood of drinking at an accelerated pace. To avoid behaving badly, the desire to present oneself as responsible, or romantic intentions might cause men to refrain from increasing drinking pace when females are present.
For both genders, engaging in predrinking made drinking at an accelerated pace about three times more likely than when not predrinking. This is surprising since the higher prices of alcoholic beverages on-premise (Forsyth, 2010) and having consumed sufficiently large amounts of alcohol during predrinking are reasons not to drink large amounts after predrinking (Wells, Graham, & Purcell, 2009). On the contrary, the present findings are not only consistent with previous evidence showing that predrinking adds to the total amount consumed on a given evening (Hughes, Anderson, Morleo, & Bellis, 2008; Labhart et al., 2013), they also indicate that even nonexcessive predrinking can be a risk factor of drinking at a fast pace later on. This study therefore suggests that even small amounts of alcohol consumed earlier in the evening while predrinking may instigate drinking at an accelerated pace afterward, leading to excessive drinking and related consequences later in the evening.
In addition, that is, irrespective of whether predrinking took place, the more time men spent in bars and other drinking establishments, the higher their likelihood of accelerated drinking pace. The constant alcohol availability in bars may be partly responsible for this finding, but also the idea that persistent exposure to drinking cues (which is usually the case in bars and drinking establishments) encourages constant drinking (Koordeman, Kuntsche, Anschutz, van Baaren, & Engels, 2011; Larsen, Lichtwarck-Aschoff, Kuntsche, Granic, & Engels, 2013). Moreover, it is known that a lack of self-regulation in people who have self-imposed drinking limits leads to more drinking, which is more likely to occur when people spend more time in a bar.
Concerning individual-specific factors, this study extends current evidence by showing that males are more likely than females to drink at an accelerated pace throughout the course of the evening. Moreover, among men who indicated a high level of enhancement motives at baseline, accelerated drinking was particularly likely.
Increased drinking pace was also observed among females who scored high on coping motives at baseline. There are different explanations for this result. On the one hand, one would expect those who usually drink to forget about their problems to do the same on Saturday evenings. On the other, females were found to use alcohol to feel more self-confident and to alleviate social anxiety and stress, for example, when meeting new people on a night out. This is consistent with the result that the presence of males did not decrease the likelihood of accelerated drinking for females in the same way as the presence of females had for males. In contrast, women who scored high on conformity motives at baseline were less likely to have evenings with increased drinking pace. This is consistent with both conceptual consideration and empirical evidence on alcohol consumption in general. Conceptually, having one or two drinks is often sufficient to fit in with a group one likes or not to feel rejected because of nondrinking. Empirically, conformity motives have been consistently found to be negatively related to heavy episodic drinking in a number of studies (Cooper, 1994; Kuntsche et al., 2014; Kuntsche & Labhart, 2013a; Kuntsche, Stewart, & Cooper, 2008).
Limitations and Strengths
Although participants were recruited from several institutions in the two major cities, the nonrandom sample of cell phone users, which may not be representative of young adults in Switzerland, is a limitation of the study. While our decision to select a two-group solution was supported by a combination of statistical as well as theoretical arguments, this does not rule out the possibility that a different number of trajectories would arise with larger samples or in other cultures. The fact that the entire study was based on self-reports is another limitation. While the very short timeframes (mostly 60 min) minimized recall bias due to memory deficits (Kuntsche & Labhart, 2012) and average response times were generally low (Kuntsche & Labhart, 2013b), we cannot guarantee the precision of the participants’ responses, particularly later at night and bearing in mind their increasing state of intoxication. Despite the fact that we have included the number of male and female friends present and the time spent in bars as evening-level predictors, there are many other factors potentially explaining accelerated drinking on the given evening such as having something to celebrate, being at a party or festival, or meeting particular people (best friends, colleagues, sport team members, etc.). These issues clearly demonstrate that further studies with even larger samples, possibly also from other cultures and using further evening-level predictors and complementary data collection methods such as qualitative interviews, are needed to confirm and to provide further insights into the reported evening drinking patterns and associated determinants.
One of the study’s strengths is its complex, and to our knowledge unique, design, that is, using participants’ cell phones to collect event-level data six times per evening over five subsequent Saturdays. Participants appreciated the conciseness of the Internet-stored questionnaires and the fact that they could be easily answered anywhere in real time (Kuntsche & Labhart, 2013b). The fact that cell phones could be used independently of the specific operating system, which is not usually the case with cell phone applications, is another advantage (Kuntsche & Labhart, 2013b).
Implications for Preventive Action
Within these limitations, the results have important implications to prevent accelerated drinking pace (a) in general, (b) by targeting situational determinants, and (c) by taking account of individual characteristics. In general, it appears important to make people aware that there are evenings when they increase the number of drinks consumed from one hour to the next. Since this is clearly not the norm (i.e., almost two thirds of Saturday evenings were characterized by stable low drinking in this sample of young adult drinkers), providing personalized normative feedback in brief motivational interventions (Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Grossberg et al., 2010; Walters, Vader, Harris, Field, & Jouriles, 2009) appears useful to impede acceleration of drinking pace. Goal setting (e.g., aiming not to drink more than 1 drink per hour), goal commitment, and reinforcing self-efficacy for goal achievement (Lozano & Stephens, 2010; Voogt, Poelen, Kleinjan, Lemmers, & Engels, 2014) are also promising strategies in this respect. For this purpose, it is important that people self-monitor and keep track of their drinking behavior throughout the course of the evening (Grossberg et al., 2010; Otten et al., 2014).
Since an accelerated drinking pace was found when same-sex friends were present, resistance skills training designed to impede the modeling of alcohol use and to reinforce drinking refusal self-efficacy and resistance to offers of alcohol by peers (Botvin, Griffin, & Murphy, 2011; Griffin & Botvin, 2010; Voogt et al., 2014; Witkiewitz, Donovan, & Hartzler, 2012) can be a promising strategy in impeding the acceleration of drinking pace on Saturday evenings. Since such a drinking pattern was also found when spending a great deal of time in bars and other drinking establishments, the implementation of structural measures such as banning happy hours, making nonalcoholic beverages available at a low price, and training staff not to serve alcoholic beverages to inebriated patrons, appear important to promote safer drinking environments (Homel, Carvolth, Hauritz, McIlwain, & Teague, 2004; Measham, 2006).
ConclusionsBased on an innovative design, that is, using participants’ cell phones to collect event-level data six times per evening over five subsequent Saturdays, this study identified a group of evenings (35.2%) during which participants consumed an increasing number of drinks each hour from before 8 p.m. until after midnight, resulting in a total consumption of more than 11 drinks per evening on average. This is a concern in terms of individual and public health due to a number of particularly detrimental consequences, which were confirmed in this study. Concerning evening predictors, drinking at an accelerated pace was more likely when same-sex friends were present (presence of women was protective for men) and when a great deal of time was spent in bars and other drinking establishments. Concerning individual predictors, men were more likely than women to accelerate the pace of their drinking over the course of the evening, as were those who indicated a high level of enhancement motives at baseline. Women scoring high on coping motives were also more likely to engage in accelerated drinking. Providing personalized normative feedback about Saturday evening drinking patterns, goal setting, goal commitment, reinforcing self-efficacy for goal achievement, resistance skills training, and promotion of safer drinking environments are likely to impede acceleration of drinking pace on Saturday evenings.
Footnotes 1 Results not shown but available from the authors on request.
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Submitted: July 23, 2014 Revised: December 12, 2014 Accepted: December 14, 2014
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Record: 29- Intimate partner violence and specific substance use disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Smith, Philip H.; Homish, Gregory G.; Leonard, Kenneth E.; Cornelius, Jack R.; Psychology of Addictive Behaviors, Vol 26(2), Jun, 2012 pp. 236-245. Publisher: American Psychological Association; [Journal Article] Abstract: [Correction Notice: An Erratum for this article was reported in Vol 26(2) of Psychology of Addictive Behaviors (see record 2012-09243-001). There is an error in the last sentence in the first paragraph of the Results section. The corrected sentence is presented in the erratum.] The association between substance use and intimate partner violence (IPV) is robust. It is less clear how the use of specific substances relates to relationship violence. This study examined IPV perpetration and victimization related to the following specific substance use disorders: alcohol, cannabis, cocaine, and opioid. The poly substance use of alcohol and cocaine, as well as alcohol and marijuana, were also examined. Data were analyzed from wave two of the National Epidemiologic Survey on Alcohol and Related Conditions (2004–2005). Associations between substance use disorders and IPV were tested using logistic regression models while controlling for important covariates and accounting for the complex survey design. Alcohol use disorders and cocaine use disorders were most strongly associated with IPV perpetration, while cannabis use disorders and opioid use disorders were most strongly associated with IPV victimization. A diagnosis of both an alcohol use disorder and cannabis use disorder decreased the likelihood of IPV perpetration compared to each individual substance use disorder. A diagnosis of both an alcohol use disorder and cocaine use disorder increased likelihood of reporting IPV perpetration compared with alcohol use disorders alone but decreased likelihood of perpetration compared with a cocaine use disorder diagnosis alone. Overall, substance use disorders were consistently related to intimate partner violence after controlling for important covariates. These results provide further evidence for the important link between substance use disorders and IPV and add to our knowledge of which specific substances may be related to relationship violence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Intimate Partner Violence and Specific Substance Use Disorders: Findings From the National Epidemiologic Survey on Alcohol and Related Conditions
By: Philip H. Smith
Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, The State University of New York;
Gregory G. Homish
Department of Community Health and Health Behavior, School of Public Health and Health Professions, and Research Institute on Addictions, University at Buffalo, The State University of New York
Kenneth E. Leonard
Research Institute on Addictions and Department of Psychiatry, University at Buffalo, The State University of New York
Jack R. Cornelius
Department of Psychiatry, University of Pittsburgh
Acknowledgement: The research and manuscript were supported by a grant from the ABMRF/The Foundation for Alcohol Research, awarded to Gregory G. Homish.
See page 254 for a correction to this article.
The likelihood that an individual will experience intimate partner violence (IPV) during their lifetime is high (Breiding, Black, & Ryan, 2008b; Coker et al., 2002). Based on the National Violence Against Women Survey, Coker and colleagues (2002) estimated that the lifetime prevalence of psychological, physical, or sexual IPV was 28.9% for women and 22.9% for men. Using data from the Behavioral Risk Factor Surveillance System, Breiding et al. (2008b) found that 29.4% of women and 15.9% of men reported at least one lifetime occurrence of physical or sexual IPV. IPV victimization is associated with numerous adverse health outcomes, such as current poor health, depressive symptoms, chronic disease, chronic mental illness, injury, posttraumatic stress disorder, and HIV risk (Breiding, Black, & Ryan, 2008a; Campbell, 2002; Coker et al., 2002; Hill, Schroeder, Bradley, Kaplan, & Angel, 2009; Johnson & Leone, 2005). While there is strong evidence that substance use is both a risk factor and outcome associated with IPV (Caetano, McGrath, Ramisetty-Mikler, & Field, 2005; El-Bassel, Gilbert, Wu, Go, & Hill, 2005; Leonard, 1993; Stuart, Temple, & Moore, 2007), our understanding of the association between specific substances and IPV is limited. A greater understanding of these associations will potentially allow intervention and prevention efforts to focus more specifically on the substances most closely associated with relationship violence.
Specific Substance Use and Intimate Partner Violence Perpetration
Experimental research has tested a direct psychopharmacologic link between specific substance use and IPV, but the strength of the findings varies by drug class. For example, there is robust evidence that alcohol intoxication increases aggression (Chermack & Giancola, 1997), but findings for marijuana and cocaine are equivocal (Hoaken & Stewart, 2003). These experimental studies are limited in that they only test a direct pathway between acute substance use and aggressive behavior. Thus, insignificant findings for a specific drug type do not necessarily imply that use of this drug does not contribute to relationship violence. There are other pathways through which use of specific substances may lead to relationship violence perpetration (Leonard, 1993; T. M. Moore et al., 2008), and observational research may be better equipped to tap into these mechanisms.
There is a large body of observational research examining the association between specific substances and IPV perpetration (Foran & O'Leary, 2008; Leonard, 1993; T. M. Moore et al., 2008); however, the consistency of the findings varies with regard to specific substances. There is strong and consistent evidence that alcohol use is associated with intimate partner violence (Foran & O'Leary, 2008; Leonard, 1993), notwithstanding the few studies that do not find evidence of this effect (Feingold, Kerr, & Capaldi, 2008). Findings on the relation between specific illicit drugs and IPV perpetration are less consistent (Feingold et al., 2008; Murphy, O'Farrell, Fals-Stewart, & Feehan, 2001; Stuart et al., 2008). The majority of these studies use treatment- or community-based samples of convenience, each of which has important limitations. Treatment samples tend to be relatively homogenous and are not necessarily representative of the population of individuals with substance use disorders; thus, findings may lack generalizability. Findings from community samples tend to be more broadly applicable, but the prevalence of use disorders with respect to specific substances is often too uncommon in these samples to be studied in any meaningful way. As a solution, most community samples have collapsed drug use into a single variable or measured general drug use rather than substance use disorders.
Population-based samples may be particularly advantageous for addressing the relation between specific substances and intimate partner violence. These samples may be large enough to assess the problematic use of individual substances, while at the same time providing a high degree of external validity. A few previous studies have examined substance use and partner violence perpetration using population based samples (Anderson, 2002; Caetano et al., 2005; Cunradi, Caetano, & Schafer, 2002; Kantor & Straus, 1987; Stalans & Ritchie, 2008); however, some only examined alcohol (Caetano et al., 2005; Kantor & Straus, 1987), while others collapsed drug use into a single category (Anderson, 2002; Cunradi et al., 2002). Stalans and Ritchie (2008) examined associations between specific substance use types and relationship violence using data from the National Household Survey on Drug Abuse. Their findings indicated that in the overall sample marijuana abuse or dependence was not associated with IPV perpetration, while a significant association was found for past year stimulant use. These findings provide insight into the relation between specific substance use and IPV perpetration; however, the authors were unable to test associations between the abuse of specific stimulant drugs and IPV perpetration, and the IPV outcome measure only included hitting, leaving out other types of physically violent behavior. In an attempt to summarize previous findings across a diverse range of studies, Moore and colleagues (2008) conducted a comprehensive meta-analysis on the relation between specific substance use and IPV perpetration. With regard to physical IPV perpetration, significant effects were found for cocaine and opiates, while the effects for sedatives, marijuana, stimulants, and hallucinogens were not statistically significant.
Moore and colleagues (2008) also called attention to the need for research on how poly substance use contributes to relationship violence. Substance users often use combinations of substances or substances in sequence (S. C. Moore, 2010), and comorbid substance use may differentially impact relationship violence compared to the use of individual substances. Because of the relatively common comorbid use of these substances, it may be important to assess the impact on relationship aggression. Moore and Stuart (2004) examined the interaction between alcohol use and other drug use among men in a batterer intervention program and found a significant effect. However, Murphy et al. (2001) conducted a similar analysis among men in alcohol treatment and did not find evidence of an interaction. These studies did not assess the interaction between alcohol and individual substances and are subject to the previously noted limitations of treatment samples, which could account for the conflicting results. Research is needed to assess poly substance use in more heterogeneous populations.
Specific Substance Use and IPV Victimization
The relation between specific substance use and IPV victimization is also complex, and multiple pathways have been hypothesized (El-Bassel et al., 2005; Kilpatrick, Acierno, Resnick, Saunders, & Best, 1997). For one, outcomes associated with substance use may generally increase risk for conflict in relationships, leading to violent behavior. Substance use issues can lead to increased stress in the relationship and disputes with regard to, for example, spending money or where and with whom a couple spends time, which may manifest as violent conflict in some couples (Goldstein, 1985). Women may be particularly vulnerable to IPV victimization while they are under the influence of substance use. Also, it has been suggested that women may use drugs like marijuana and tranquilizers to self-medicate the physical and emotional pains of victimization, a factor that may operate in cross-sectional studies (Gilbert, El-Bassel, Rajah, et al., 2000; Gilbert, El-Bassel, Schilling, Wada, & Bennet, 2000; Kilpatrick et al., 1997).
As with IPV perpetration, research generally supports an association between victimization and substance use. Empirical evidence for an association between alcohol use and IPV victimization is conflicting (Breiding et al., 2008a; Coker et al., 2002; El-Bassel et al., 2005; Testa, Livingston, & Leonard, 2003; Walton et al., 2009). Findings from the small number of studies on IPV victimization and individual illicit drug categories are mixed, making it difficult to draw conclusions about these relations (Coker et al., 2002; El-Bassel et al., 2005; Kilpatrick et al., 1997; Testa et al., 2003; Walton et al., 2009). In a population-based study of U.S. couples, Coker et al. (2002) found that female IPV victimization was associated with heavy alcohol use and painkiller use, but not with other illicit drug use (the majority of which was likely marijuana). Male IPV victimization was associated with painkiller use and other drug use, but not heavy alcohol use. Although this study was unique in its assessment of specific substances, problematic use was not assessed, which may have resulted in weakened or null findings.
There is reason to speculate that multiple substance use may interact synergistically when associated with victimization. If the theory that individuals self-medicate with substance use to cope with the emotional and physical pains of IPV victimization is accurate, there is also reason to believe individuals may tap into the synergistic effects of concurrent substance use for this purpose. Thus, it is expected that multiple substance use may be associated with IPV victimization beyond the additive effects of individual substances. Research is needed to examine these effects.
The Present Study
The primary objective of this study was to examine the association between relationship violence and the problematic use of alcohol, cocaine, cannabis, and opiates. Secondary objectives were to test for gender differences in these associations, as well as to explore the effect of poly substance use on relationship violence. Data were examined from the second wave of the National Epidemiologic Survey on Alcohol and Related Conditions. This relatively large dataset, acquired from a nationally representative sample of the U.S. noninstitutionalized adult population, contains information on substance use and use disorder diagnoses. The data also contain information on relationship violence perpetration and victimization, as well as several relevant social and mental health variables, making it suitable for addressing this research question. There is little theory to give guidance to which specific substances aside from alcohol may be related to relationship violence, and so hypotheses were driven by findings from previous research. It was hypothesized that alcohol use disorders would be associated with both IPV perpetration and victimization, based on the robust evidence that alcohol is related to relationship violence. It was also hypothesized that cocaine abuse would be associated with IPV perpetration given the existing evidence for this relation. The evidence for an association between marijuana and violence perpetration is highly conflictual. The comprehensive meta-analysis conducted by Moore and colleagues (2008) found that marijuana related to psychological but not physical relationship aggression. This study examined physical relationship violence, thus it was hypothesized that marijuana abuse would not be associated with IPV perpetration. A priori hypotheses were not generated for the association between opiate abuse and IPV perpetration, for the association between specific substances and IPV victimization, for the assessment of gender differences, or for the assessment of drug interactions because of the small number of previous studies examining these topics.
Method Study Sample
A detailed account of the NESARC methodology can be found elsewhere (Grant & Kaplan, 2005; Grant, Kaplan, Shepard, & Moore, 2003). Briefly, the first wave of NESARC data was collected during 2001 and 2002, and the second during 2004 and 2005. The response rate for the first wave was 81%, and the sample of 43,093 represented the civilian, noninstitutionalized adult population in the United States. The second wave included 34,653 of the original respondents. For both waves, surveys were administered face-to-face, using computer-assisted personal interviews. Blacks, Hispanics, and young adults were oversampled, and the data were weighted to adjust for nonresponse at the household and personal levels. Based on the 2000 Decennial Census, the data were adjusted on sociodemographic variables to ensure an accurate representation of the U.S. population. This study was based on the 25,778 wave two respondents who reported being married, dating, or being in a relationship during the past year.
Measures
Substance use disorders
The NESARC survey contained the National Institute on Alcohol Abuse and Alcoholism's Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM–IV version (AUDADIS-IV) to measure substance use disorders (Grant & Dawson, 2000; Grant, Hasin, Chou, Stinson, & Dawson, 2004). This tool assesses both abuse and dependence diagnoses for alcohol and other specific substances. For a full description of these measures, see Grant et al. (2004). In total, nine different drugs/drug categories were assessed in the NESARC data set: cannabis, cocaine, heroin, opioids, tranquilizers, sedatives, hallucinogens, amphetamines, and inhalants. Questions about drug use were prefaced by requesting that respondents only reported on use not prescribed by a physician. Thus, respondents reported on the illicit use of prescription drugs, such as sedatives, and not their medical use. For this study, respondents were considered problematic substance users if they received either an abuse or dependence diagnosis. Of the drug categories assessed in NESARC, only alcohol, cocaine, cannabis, and opioids were examined for this study; other substances were not examined because of low endorsement of their use. Binge drinking was also included in this study to test for its impact relative to the AUD measure. Binge drinking was defined as consuming five or more drinks for men and four or more drinks for women in a single day, and participants reported on how frequently they drank this much during the past 12 months. Responses ranged from 1 to 11 (1 = every day, 11 = never in the last year), and were reverse coded to aid interpretability (0 = never in the last year, 10 = every day).
Two specific poly substance use combinations were chosen for analysis in this study: diagnosis of both alcohol and cocaine use disorders, and diagnosis of both alcohol and marijuana use disorders. These particular combinations were chosen because of the frequency of their use (S. C. Moore, 2010). Interaction terms were added to regression models to create groupings for these poly substance use combinations.
Intimate partner violence
Five items were used to assess physical IPV perpetration. Respondents were asked how frequently they had engaged in the violent behaviors during the past year (from 0 to 4, never to more than once per month), and then were asked how frequently they had experienced the violent behaviors from their partner. For example, participants were asked, “In the last 12 months, how often did you push, grab, or shove your spouse or partner?” and then, “How often did your spouse or partner do this to you?” For this study, responses to the five perpetration questions were combined into a single binary variable representing whether the respondent perpetrated IPV during the past year. The same was done for IPV victimization. There were several reasons for this decision. First, the response options for these questions could not be easily combined. The options included “never,” “once,” and “twice,” but then jumped to “monthly” and “more than monthly,” which precluded summing them for a frequency of violence measure. In addition, two items assessed physical aggressive acts, “pushing, grabbing, or shoving” and “slap, kick, bite, or hit,” and two items were directed at injuries attributable to violence, which may have occurred because of one of the specific actions listed above, but may have occurred because of other aggressive acts that are not captured by a limited number of items (e.g., pinched, twisted arm). As a result, it was necessary to include the injury items because they might capture additional aggressive acts, but they could not simply be added to the frequency of the aggressive acts because it would constitute double counting for some individuals. For example, a hit that left a bruise might be counted as two items. As a consequence, the more valid approach was to simply indicate whether any IPV had occurred.
Covariates
There are a number of demographic, socioeconomic, and mental health variables that could potentially account for the association between specific drug types and mental health disorders. Age, gender, race/ethnicity, education, and household income were examined as sociodemographic covariates, and antisocial symptoms and depression symptoms as mental health covariates. The NESARC survey asked a number of questions regarding depression and antisocial symptoms, to which respondents answered yes or no for whether or not they occurred during the past year for depression and during their lifetime for antisocial behavior. Affirmative responses were summed for each of these variables, creating a count variable for antisocial (0–30) and depression (0–19) symptoms.
As pointed out by Anderson (2002) and others (Johnson & Leone, 2005), a large proportion of violence in community-based samples tends to be mutual, and so victims are often perpetrators and perpetrators are often also victims. In light of evidence that both perpetration and victimization are related to substance use, it is important to consider the potential confounding effect of victimization when examining perpetration, and vice versa. Thus, victimization was examined as a covariate in perpetration models, and perpetration was examined as a covariate in the victimization models.
Statistical Analyses
All statistical analyses for this study were conducted using Stata/SE version 10.0 (StataCorp, 2007), taking into account NESARC's complex survey design (sampling, weighting scheme, etc.). Chi-square tests of independence and t tests were used to examine differences in frequencies and means of covariates and substance use variables between those reporting IPV and the remainder of the sample. Logistic regression analyses were conducted to examine associations while adjusting for potentially confounding covariates. Models were created in a stepwise fashion, and separate models were calculated for IPV perpetration and victimization. Given the correlations between perpetration and victimization, one would ideally want to assess the influence of substance use on perpetration after controlling for victimization and vice versa. However, many episodes of couple violence involve aggressive actions by both members (Anderson, 2002; Johnson, 2006). As a result, statistically controlling for victimization when examining perpetration can have the effect of removing predictable variance that is attributable to perpetration among mutually violent couples. A similar problem arises in examining victimization. Consequently, we present analyses of perpetration with and without victimization in the model. Similarly, our analyses of victimization were conducted with and without perpetration in the model. Understanding whether a specific substance is related to perpetration or victimization depends upon the full set of analyses.
For our base models, initially a main effects model was created that controlled for sociodemographic characteristics, symptoms of depression, and antisocial symptoms. Second, several interactions were tested for significance: gender by each substance use variable, alcohol use disorder by cocaine use disorder, and alcohol use disorder by cannabis use disorder. If a significant interaction was detected, simple slope analyses were conducted to generate separate odds ratios for individual groups. These same procedures were then repeated with the inclusion of victimization as a covariate in the analysis of perpetration and the inclusion of perpetration as a covariate in the analysis of victimization.
Because of listwise deletion of respondents with missing data, the full logistic model for IPV perpetration included 25,633 of the 25,778 respondents in a relationship (99.4%), and the full model for IPV victimization included 25,631 of the respondents in a relationship (99.4%). Those with missing data were older (M = 50.70 compared with 46.43) and more likely to be black, Hispanic/Latino, or other race/ethnicity. There was also a slightly lower prevalence of IPV among those with missing data (3.0% compared to 5.3% for victimization, and 3.7% compared to 5.4% for perpetration). However, it was not expected that these differences would impact the results in any meaningful way, given that less than 1% of the sample had any missing data.
Results Intimate Partner Violence
Females were slightly more likely to report past year IPV perpetration than males (6.9% and 4.0%, respectively). The reverse was true for victimization, with 5.6% of men experiencing IPV victimization compared to 5.0% of women. Of those reporting IPV perpetration, 74.9% of men and 54.3% of women also reported IPV victimization. Table 1 displays a comparison between those reporting IPV perpetration, those reporting IPV victimization, and the remainder of the sample on sociodemographic and mental health variables. Perpetrators were more likely to be female, were younger, were more likely to be of non-White race/ethnicity, had lower levels of education and household income, and displayed greater numbers of depression and antisocial symptoms (all p values <0.001). IPV victims were more likely to be female, were younger, were more likely to be of non-White race/ethnicity, had lower levels of education and household income, and displayed greater numbers of depression and antisocial symptoms (all p values <0.001).
Description of Sample by Perpetrator and Victim Status: Socio-Demographic Characteristics and Mental Health Illness Symptoms
Table 2 displays the prevalence of substance use disorders by IPV perpetrator and victim status. All substance use disorders examined (alcohol, cocaine, cannabis, opioids) were more prevalent among both IPV perpetrators and IPV victims than the remainder of the sample (all p values <0.001). Alcohol use disorders were the most prevalent use disorders among IPV perpetrators (21.7%), followed by cannabis use disorders (5.8%). Cocaine use disorders and opioid use disorders were less prevalent (2.1% and 1.5%, respectively). Alcohol use disorders were the most prevalent use disorders among IPV victims (24.6%), followed by cannabis use disorders (7.4%). Cocaine use disorders and opioid use disorders were less prevalent (2.0% and 2.4%, respectively).
Frequencies of Specific Substance Use Disorders by Perpetrator and Victim Status
Intimate Partner Violence perpetration: Logistic Regression Results
Table 3 displays the odds ratios for substance use disorder associations with IPV perpetration, calculated using logistic regression and adjusted for demographics. There were notable differences between models unadjusted for and adjusted for victimization. For the main effects models alcohol use disorders and cocaine use disorders were significantly associated with IPV perpetration both before and after controlling for victimization. Binge drinking frequency and cannabis use disorders were significantly associated with perpetration only when estimates were unadjusted for victimization. Opioid use disorders became significantly inversely associated with perpetration when estimates accounted for victimization.
Intimate Partner Violence Perpetration Results From Logistic Regression (n = 25,633)
Significant gender by substance use interactions were found for alcohol use disorders, binge drinking frequency, and cannabis use disorders both before and after including victimization in model estimates. No significant gender by substance use interactions were found for cocaine or opioid use disorders. Without controlling for victimization, the association with alcohol use disorders was significant for both males and females, although the association was stronger for females than males. When victimization was accounted for in estimates, the association was no longer significant for males. Binge drinking frequency was associated with IPV perpetration for females but not males regardless of whether victimization was included in the model. For males, the association with cannabis use disorders was nonsignificant without controlling for victimization, and inversely significant when victimization was accounted for. For females, the association was no longer significant when estimates accounted for victimization.
Intimate Partner Violence Victimization: Logistic Regression Results
Table 4 displays the odds ratios for substance use disorder associations with IPV victimization, calculated using logistic regression and adjusted for demographics. There were notable differences between models unadjusted for and adjusted for perpetration. For the main effects models, alcohol use disorders, frequency of binge drinking, and cannabis use disorders were significantly associated with IPV victimization both before and after controlling for perpetration. Opioid use disorders became significantly associated with victimization after controlling for perpetration. Cocaine use disorders became significantly inversely associated with victimization after controlling for perpetration.
Intimate Partner Violence Victimization Results From Logistic Regression (n = 25,631)
In models that did not account for perpetration, significant gender by use disorder interactions were found for all substances except cocaine. The associations with alcohol use disorders and cannabis use disorders were significant for both males and females, with odds ratios that were slightly lower for males than females. The associations with binge drinking and opioid use disorders were significant only for females. There were no significant gender by use disorder interactions when victimization was statistically controlled. However, the interaction odds ratio for opioid use disorders was close to significant (p = .052), with a significant, positive association for females and no association for males.
Poly Substance Use Interactions
Results from the logistic regression analysis of poly substance use interactions are shown in Table 5. These odds ratios were adjusted for demographic covariates. With regards to IPV perpetration, a significant interaction was found for both alcohol use disorder by cocaine use disorder and alcohol use disorder by cannabis use disorder. Alcohol use disorder without cocaine use disorder and cocaine use disorder without alcohol use disorder were both significantly associated with IPV perpetration, regardless of whether models adjusted for victimization. Evidence was found that those with both a cocaine use disorder and alcohol use disorder had greater odds of reporting IPV perpetration than those with an alcohol use disorder only, although the association was statistically significant only in the model adjusting for victimization. Conversely, having both use disorders was associated with decreased odds of reporting IPV perpetration compared to those with a cocaine use disorder only. Again, this was only significant in the model adjusting for victimization. With regards to cannabis, the general finding was that alcohol use disorders alone and cannabis use disorders alone were associated with increased odds of IPV perpetration; however, having comorbid use disorders decreased the odds of IPV perpetration relative to each individual use disorder.
Poly-Substance Use and Relationship violence: Testing Interactions Between Multiple Substance Use disorders
With regard to IPV victimization, no evidence was found for an interaction between alcohol and cocaine. In the model controlling for perpetration, the interaction between cannabis use disorder and alcohol use disorder was statistically significant. Having combined alcohol and cannabis use disorders was associated with increased odds of IPV victimization relative to each individual substance.
DiscussionThis study examined the associations between specific substance use disorders and intimate partner violence, using data from wave two of NESARC. The findings substantiated our understanding of the role illicit drug use plays in relationship violence. The NESARC dataset was large enough to examine these relations and produce nationally generalizable results. Consistent patterns emerged for the individual substances examined in this study, some of which differed by gender.
One of the key aspects of the results is to understand the interdependent nature of perpetration and victimization in the context of partner violence. It is often the case that aggressing against one's partner and receiving aggression from one's partner are linked at the level of the incident. As a result, if a drug were to have an acute effect only on the perpetration of violence, it would necessarily have a relationship both with perpetration directly and influence the relationship with victimization indirectly through the impact of perpetration on the other person's defensive or retaliatory response. If nearly every perpetration were followed by an aggressive response by the other person, controlling for victimization in the analysis would remove a substantial portion of the variance in perpetration. Consequently, understanding the role of substance use in partner violence requires that we examine both perpetration and victimization, both independently as well as controlling for each other.
Cocaine Use Disorders
Perhaps the clearest picture emerges for the association between cocaine use disorders and partner violence. Our hypothesis that cocaine use disorders would be associated with relationship violence perpetration was confirmed, and the result was consistent for both males and females, with and without controlling for victimization. Moreover, this relationship is actually strengthened after controlling for victimization. This provides robust evidence that problematic cocaine use is associated with relationship violence perpetration. In a recent meta-analysis of the association between illicit drug use and IPV perpetration, Moore et al. (2008) found that cocaine use had the largest effect size compared with other drug use, and our results support this finding.
Conversely, cocaine use disorders were not related to victimization in the unadjusted model but were related to a reduced likelihood of IPV victimization for both men and women in the model adjusted for perpetration. There is little previous research with which to compare this finding. El-Bassel et al. (2005) found some evidence that women using cocaine were more likely to be victims of relationship violence; however, their study used a treatment sample of women seeking methadone, and it is difficult to draw comparisons across these highly different study samples. One can speculate that the positive association with perpetration and inverse association with victimization are attributable to the psychopharmacologic effects of cocaine use, but alternative explanations cannot be ruled out because of this study's design.
Opioid Use Disorders
The results relative to opiate use and partner violence also provide a fairly consistent pattern. Opioid use disorders were not associated with violence in the unadjusted models. However, they were associated with a decreased likelihood of violence perpetration for both men and women when victimization was added to the model. Conversely, opioid use disorders were positively associated with victimization. However, the interaction of opioid disorders and gender, which was significant in the unadjusted model and marginal (p = .052) when perpetration was controlled, indicated that opioid disorders were associated with an increased risk of victimization for women. This may indicate that opioid disorders increase the likelihood of victimization for women, or that victimization leads to opioid use for women. Female victims of relationship violence tend to experience more injury and psychological distress than men (Stets & Straus, 1990), which might account for the significant association of opioid use disorders and victimization among women but not among men.
Marijuana
In their meta-analysis, Moore et al. (2008) found that marijuana use was associated with psychological but not physical IPV perpetration. Physical violence was the outcome of interest in this study; thus, we hypothesized a null association between marijuana and IPV perpetration. Our findings were mixed, based on gender differences and whether models accounted for variance associated with IPV victimization. The interaction between cannabis use disorders and gender was significant in both the unadjusted and the models adjusting for victimization. For women, marijuana was associated with increased perpetration, although the association when controlling for victimization did not reach statistical significance (p = .08). This may suggest that marijuana use is more strongly implicated in mutual relationship violence than independently perpetrated violence for women. The association was inverse and significant for men when victimization was entered into the model, suggesting that heavy, problematic marijuana use may decrease the likelihood of nonreciprocated violence. Marijuana use disorders were robustly associated with IPV victimization, for both men and women. Previous researchers have speculated that IPV victims may self-medicate with substance use to cope with the effects of violence (El-Bassel et al., 2005; Testa et al., 2003); therefore, it is possible these associations with marijuana are attributable to the analgesic effects of acute use.
Alcohol Use Disorders
There is a large body of literature implicating problematic alcohol use as a risk factor for relationship violence (Foran & O'Leary, 2008; Leonard, 1993). Thus, we hypothesized that alcohol use disorders would be associated with both violence perpetration and victimization. The results were generally consistent with this expectation. Alcohol use disorders were robustly associated with IPV perpetration and victimization. However, there was also an interaction between AUD and gender, both for perpetration and victimization, suggesting that the effect of AUD, although significant for women and men, was stronger for women than for men. This interaction was not significant for victimization when perpetration was statistically controlled, suggesting that it might be applicable primarily for mutual violence. The interaction remained significant for perpetration while controlling for victimization and indicated that the association between AUD and perpetration was significant for women, but not for men. Again, inasmuch as there is a very strong relationship between perpetration and victimization, these findings suggest that AUD is related to mutual IPV among men (the relationship held when victimization was controlled) but may be less relevant for male-only violence.
The findings with respect to the relationship between frequency of binge drinking and IPV were somewhat different. These analyses suggested that binge drinking was associated with women's perpetration and that this relationship was not affected by controlling for victimization. For men, binge drinking was not related to perpetration. For both men and women, binge drinking was related to victimization, irrespective of perpetration. The pattern of findings suggests that binge drinking was specifically related to women's perpetration and associated with men's and women's victimization.
Poly Substance Use
The exploratory analyses for specific drug interactions uncovered some interesting patterns. For the alcohol by cocaine interaction, findings suggest that having both an alcohol and cocaine use disorder increased risk for violence perpetration relative to having only an alcohol use disorder, but decreased risk for violence perpetration relative to having only a cocaine use disorder. It seems plausible that this reflects a patterning of use in which alcohol is used, either simultaneously or subsequent to cocaine use to modulate the effect of the cocaine. This pattern has been reported to be fairly common (S.C. Moore, 2010). Similarly, the combination of cannabis and alcohol use disorders decreased risk relative to both individual use disorders, possibly suggesting that the synergistic effects of simultaneous cannabis and alcohol consumption decrease risk for aggressive behavior. However, it is important to note that explanations based on simultaneous use are purely speculative, as NESARC did not assess whether these substances were used simultaneously. Clearly, more research is needed to understand the joint impact of these substance use disorders with regards to violent behavior.
Limitations
This study was subject to some limitations. As previously noted, the study design for the current report was cross-sectional, which does not allow analyses to establish temporal relationships between variables. Although NESARC is a two-wave longitudinal study, intimate partner violence was assessed differently at waves one and two, making a comparison between waves methodologically unsound. The wave two data were chosen for this study because the assessment of IPV was more comprehensive than the first wave. Past research has focused on substance use as a risk factor for IPV perpetration and both predictors and outcomes related to victimization, but this directionality cannot be supported or refuted by this study. Taken in context with previous research, the findings from this study provide valuable information on the relationship between specific substance use and IPV. Also, intimate partner violence was only measured at the individual and not the couple level, thus there was no way to verify the respondents' reports of either IPV victimization or perpetration. This may be important given that relationship violence tends to be underreported in survey research (Dutton & Hemphill, 1992). It would have been difficult for the NESARC study to evaluate couple-level variables and still obtain such a large sample size, and it is the large sample size that allowed this study to examine specific substance use disorders for both men and women, and for both IPV perpetration and victimization. Future longitudinal research that assesses these variables at the couple level can build on this study's findings. Lastly, those with a predisposition to act aggressively may be less likely to be in a relationship at any one time point than others, thus a limitation of this sample is that aggressive individuals may be underrepresented in the study sample.
Conclusions
All substance use disorders examined in this study were related to intimate partner violence after controlling for important covariates. There were several differences between results when models were adjusted or unadjusted for the interdependence between IPV perpetration and victimization, and the comparison of these findings provides insight into possible differences between mutually violent couples and couples where only one partner is physically violent. The findings from this study, especially when adjusting for the correlation between victimization and perpetration, were largely consistent with what might be expected when considering the psychopharmacological effects of the drugs. Alcohol and cocaine were most strongly associated with intimate partner violence, while cannabis and opioid analgesics were most strongly associated with victimization. Conversely, associations with victimization were weak or inverse for cocaine, while associations with perpetration were weak or inverse for marijuana and opioids. It is impossible to conclusively determine the mechanisms underlying the findings of this study, but this consistency is certainly worth noting and may indicate that the psychopharmacologic effects of drugs are more strongly implicated when only one partner is violent, while other mechanisms such as conflict surrounding drug use may be more strongly related to mutual IPV. When significant gender effects were detected, associations tended to be stronger for women than for men, especially with regard to victimization. Poly substance use effects for specific combined use disorders were also detected, highlighting the importance of the further exploration of these findings. Overall, this study supported the continued exploration of possible mechanisms underlying the association between substance use and relationship violence, as well as the simultaneous treatment of these problematic behaviors (Stuart et al., 2007).
Footnotes 1 We thank an anonymous reviewer for suggesting the presentation of models with and without accounting for the correlation between IPV perpetration and victimization.
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Submitted: October 13, 2010 Revised: June 8, 2011 Accepted: June 17, 2011
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Source: Psychology of Addictive Behaviors. Vol. 26. (2), Jun, 2012 pp. 236-245)
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Record: 30- 'Intimate partner violence and specific substance use disorders: Findings from the national epidemiologic survey on alcohol and related conditions': Correction to Smith et al. (2011). Smith, Philip H.; Homish, Gregory G.; Leonard, Kenneth E.; Cornelius, Jack R.; Psychology of Addictive Behaviors, Vol 26(2), Jun, 2012 pp. 254. Publisher: American Psychological Association; [Erratum/Correction] Abstract: Reports an error in 'Intimate partner violence and specific substance use disorders: Findings from the national epidemiologic survey on alcohol and related conditions' by Philip H. Smith, Gregory G. Homish, Kenneth E. Leonard and Jack R. Cornelius (Psychology of Addictive Behaviors, Advanced Online Publication, Aug 8, 2011, np). There is an error in the last sentence in the first paragraph of the Results section. The corrected sentence is presented in the erratum. (The following abstract of the original article appeared in record 2011-16753-001.) The association between substance use and intimate partner violence (IPV) is robust. It is less clear how the use of specific substances relates to relationship violence. This study examined IPV perpetration and victimization related to the following specific substance use disorders: alcohol, cannabis, cocaine, and opioid. The poly substance use of alcohol and cocaine, as well as alcohol and marijuana, were also examined. Data were analyzed from wave two of the National Epidemiologic Survey on Alcohol and Related Conditions (2004–2005). Associations between substance use disorders and IPV were tested using logistic regression models while controlling for important covariates and accounting for the complex survey design. Alcohol use disorders and cocaine use disorders were most strongly associated with IPV perpetration, while cannabis use disorders and opioid use disorders were most strongly associated with IPV victimization. A diagnosis of both an alcohol use disorder and cannabis use disorder decreased the likelihood of IPV perpetration compared to each individual substance use disorder. A diagnosis of both an alcohol use disorder and cocaine use disorder increased likelihood of reporting IPV perpetration compared with alcohol use disorders alone but decreased likelihood of perpetration compared with a cocaine use disorder diagnosis alone. Overall, substance use disorders were consistently related to intimate partner violence after controlling for important covariates. These results provide further evidence for the important link between substance use disorders and IPV and add to our knowledge of which specific substances may be related to relationship violence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Correction to Smith et al. (2011)
In the article “Intimate Partner Violence and Specific Substance Use Disorders: Findings From the National Epidemiologic Survey on Alcohol and Related Conditions” by Philip H. Smith, Gregory G. Homish, Kenneth E. Leonard, and Jack R. Cornelius (Psychology of Addictive Behaviors, Advance online publication, August 8, 2011. doi: 10.1037/a0024855), there is an error in the first paragraph of the Results section. The last sentence of the first paragraph reads: “IPV victims were more likely to be female…”, and should have read, “IPV victims were more likely to be male…”.
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Record: 31- 'Is it beneficial to have an Alcoholics Anonymous sponsor': Correction to Tonigan and Rice (2010). Tonigan, J. Scott; Rice, Samara L.; Psychology of Addictive Behaviors, Vol 27(2), Jun, 2013 Special Issue: Neuroimaging Mechanisms of Change in Psychotherapy for Addictive Behaviors. pp. 465. Publisher: American Psychological Association; [Erratum/Correction] Abstract: Reports an error in 'Is it beneficial to have an alcoholics anonymous sponsor' by J. Scott Tonigan and Samara L. Rice (Psychology of Addictive Behaviors, 2010[Sep], Vol 24[3], 397-403). There was an error in the Method section, under the Participants paragraph. The sentence 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n 68) and as they presented for outpatient substance abuse treatment ( n = 185).' is incorrect. This sentence should have read 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n = 68), as they presented for outpatient substance abuse treatment ( n = 87), and through word of mouth and advertisements ( n = 98).' (The following abstract of the original article appeared in record 2010-19026-004.) Alcoholics Anonymous (AA) attendance is predictive of increased abstinence for many problem drinkers and treatment referral to AA is common. Strong encouragement to acquire an AA sponsor is likewise typical, and findings about the benefits associated with social support for abstinence in AA support this practice, at least indirectly. Despite this widespread practice, however, prospective tests of the unique contribution of having an AA sponsor are lacking. This prospective study investigated the contribution of acquiring an AA sponsor using a methodologically rigorous design that isolated the specific effects of AA sponsorship. Participants were recruited from AA and outpatient treatment. Intake and follow-up assessments included questionnaires, semi-structured interviews, and urine toxicology screens. Eligibility criteria limited prior treatment and AA histories to clarify the relationship of interest while, for generalizability purposes, broad substance abuse criteria were used. Of the 253 participants, 182 (72%) provided complete data on measures central to the aims of this study. Overall reductions in alcohol, marijuana, and cocaine use were found over 12-months and lagged analyses indicated that AA attendance significantly predicted increased abstinence. During early AA affiliation but not later logistic regressions showed that having an AA sponsor predicted increased alcohol-abstinence and abstinence from marijuana and cocaine after first controlling for a host of AA-related, treatment, and motivational measures that are associated with AA exposure or are generally prognostic of outcome. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Correction to Tonigan and Rice (2010)
In the article “Is it beneficial to have an Alcoholics Anonymous sponsor?” by J. Scott Tonigan and Samara L. Rice (Psychology of Addictive Behaviors, Vol. 24, No. 3, pp. 397–403), there was an error in the Method section, under the Participants paragraph. The sentence “The parent study recruited 253 alcohol dependent adults from community-based AA (n = 68) and as they presented for outpatient substance abuse treatment (n = 185).” is incorrect. This sentence should have read “The parent study recruited 253 alcohol dependent adults from community-based AA (n = 68), as they presented for outpatient substance abuse treatment (n = 87), and through word of mouth and advertisements (n = 98).”
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Record: 32- Is it beneficial to have an Alcoholics Anonymous sponsor? Tonigan, J. Scott; Rice, Samara L.; Psychology of Addictive Behaviors, Vol 24(3), Sep, 2010 pp. 397-403. Publisher: American Psychological Association; [Journal Article] Abstract: [Correction Notice: An Erratum for this article was reported in Vol 27(2) of Psychology of Addictive Behaviors (see record 2013-21666-003). There was an error in the Method section, under the Participants paragraph. The sentence 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n 68) and as they presented for outpatient substance abuse treatment ( n = 185).' is incorrect. This sentence should have read 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n = 68), as they presented for outpatient substance abuse treatment ( n = 87), and through word of mouth and advertisements ( n = 98).'] Alcoholics Anonymous (AA) attendance is predictive of increased abstinence for many problem drinkers and treatment referral to AA is common. Strong encouragement to acquire an AA sponsor is likewise typical, and findings about the benefits associated with social support for abstinence in AA support this practice, at least indirectly. Despite this widespread practice, however, prospective tests of the unique contribution of having an AA sponsor are lacking. This prospective study investigated the contribution of acquiring an AA sponsor using a methodologically rigorous design that isolated the specific effects of AA sponsorship. Participants were recruited from AA and outpatient treatment. Intake and follow-up assessments included questionnaires, semi-structured interviews, and urine toxicology screens. Eligibility criteria limited prior treatment and AA histories to clarify the relationship of interest while, for generalizability purposes, broad substance abuse criteria were used. Of the 253 participants, 182 (72%) provided complete data on measures central to the aims of this study. Overall reductions in alcohol, marijuana, and cocaine use were found over 12-months and lagged analyses indicated that AA attendance significantly predicted increased abstinence. During early AA affiliation but not later logistic regressions showed that having an AA sponsor predicted increased alcohol-abstinence and abstinence from marijuana and cocaine after first controlling for a host of AA-related, treatment, and motivational measures that are associated with AA exposure or are generally prognostic of outcome. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Is It Beneficial to Have an Alcoholics Anonymous Sponsor?
By: J. Scott Tonigan
Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico;
Samara L. Rice
Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico
Acknowledgement: This research was supported by Grants K02–AA00326 and R01AA014197 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The views expressed are those of the authors and do not necessarily represent the views of the NIAAA.
Twelve-step (TS) therapy, based on Alcoholics Anonymous (AA) doctrine and practice, is the prevailing alcohol treatment model in the United States, and a primary objective of TS therapy is to facilitate community-based AA affiliation. Four meta-analytic reviews have provided relatively consistent estimates of the magnitude of AA-related benefit in terms of frequency of AA attendance and increased abstinence, for example, rw = .31 ( Emrick, Tonigan, Montgomery, & Little, 1993; Forcehimes & Tonigan, 2008; Tonigan, 2001; Tonigan, Toscova, & Miller, 1995), and prospective studies indicate that AA-related benefit includes both reduced drinking intensity (e.g., Kelly, Stout, Zywiak, & Schneider, 2006) and increased abstinence (e.g., Fiorentine & Hillhouse, 2000; Moos & Moos, 2006). Given these findings, attention has focused on the investigation of the mechanisms that account for AA-related benefit.
Social support for abstinence in AA is an important factor accounting for AA-related benefit (see Groh, Jason, & Keys, 2008, for a review). Humphreys & Noke (1997), for example, first reported that TS involvement in a Veterans Administration (VA) sample was associated with enlarged social networks supportive of abstinence at 1-year follow-up, a finding that has been replicated with a broader based sample of adult substance abusers seeking outpatient treatment (Kaskutas, Bond, & Humphreys, 2002). Work also has shown that social support for abstinence statistically mediated the positive and significant relationship between AA involvement and substance use reductions (e.g., Humphreys, Huebsch, Finney, & Moos, 1999; Laudet, Cleland, Magura, Vogel, & Knight, 2004). Here, evidence suggests that both structural aspects of AA social networks differentially influence and mediate increased abstinence among TS participants (Bond, Kaskutas, & Weisner, 2003), and that AA social networks may be more beneficial than non-AA social networks during early efforts to change behavior (Kaskutas et al., 2002).
AA sponsorship represents the intersection between the social network supportive of abstinence and the purported active ingredients of AA, working the TS ( AA World Services, 2001). Defined, the primary role of an AA sponsor is to guide a junior member through the prescribed TS of AA; a role identified in approved AA literature and recently confirmed in a qualitative study of the perceived roles of 38 AA sponsors (Whelan, Marshall, Ball, & Humphreys, 2009). In this endeavor, two AA members may have frequent social contact outside of AA meetings, and it is commonly recommended that AA members contact a sponsor when abstinence is at risk. Given the importance of general abstinence social support in AA it seems on face value that the sponsor/sponsee dyad would be important for mobilizing and sustaining increased abstinence.
What is known about the prevalence, practice, and benefit associated with AA sponsorship? Regarding prevalence, Caldwell and Cutter (1998) reported that 75% of the adults in TS treatment had a sponsor in the first 3-months after treatment. This figure is consistent with the 2007 Triennial AA survey that reported that 73% of new AA members acquire an AA sponsor within a 90-day period (AA General Service Office, 2007). It seems, however, that although a majority of AA exposed adults may initially acquire an AA sponsor this percentage decays over the course of 12 months. In Project MATCH, for example, about one in five participants (17.5%) reported having an AA sponsor at 9-month follow-up (Tonigan, Connors, & Miller, 2003), and Kaskutas et al. (2002) found in a naturalistic study of the 10 largest treatment centers in northern California that about 26% of the participants reported having an AA sponsor at 1-year follow-up. Consistent with these estimates, Mankowski, Humphreys, and Moos (2001) reported that 19.7% of a large VA sample indicated talking with an AA sponsor once or several times per week whereas 73.5% indicated that they never talked with an AA sponsor.
Predictably, AA sponsorship is positively associated with other AA-related prescribed behaviors and practices. Kelly and Moos (2003), for example, reported that acquiring an AA sponsor during treatment was a significant predictor of continued AA attendance at 1-year follow-up. Likewise, Morgenstern, Kahler, Frey, and Labouvie (1996) found that talking with a sponsor was significantly associated with working the TS, seeking the advice of other AA members, doing AA-prescribed service work, and prayer. Expanding this list, Thomassen (2002) found significant and positive associations between having a sponsor, reading aloud at AA meetings, and using the phone to talk with other AA members, and Pagano, Friend, Tonigan, and Stout (2004) reported that having an AA sponsor was predictive of later helping behaviors, with such behaviors defined as working with other alcoholics. Finally, at a more global level in investigating social support for abstinence in AA among female members in general (Rush, 2002) and residents of an Oxford House residential program in particular (Majer, Jason, Ferrari, Venable, & Olson, 2002) it has been reported that having a sponsor was significantly predictive of increased perceptions of social and personal social support.
Despite identifying 18 studies that, to some degree, investigated AA sponsorship, the actual benefits specific to having a TS sponsor are not clear. In a retrospective cross-sectional and community-based study of AA members, for example, Sheeren (1988) reported that relapse was significantly more likely when AA members did not have a sponsor and/or when they reported accessing their sponsor less often. Bond et al. (2003) provided a more rigorous test of this question by examining the bivariate associations between having a sponsor and abstinence in the 90-day period before a 1- and 3-year follow-up interview. Having a sponsor significantly and positively covaried with abstinence at both 1- and 3-year follow-ups (1 year: 42% abstinent with sponsor, 13% abstinent without sponsor; 3 year: 36% abstinent with sponsor, 12% abstinent without sponsor). Witbrodt and Kaskutas (2005) improved on this covariation strategy by statistically controlling for a host of correlated AA-related behaviors when examining associations between sponsorship and abstinence, for example, reading AA literature. Here, they reported that across several substance dependence categories, having an AA sponsor significantly increased the odds of abstinence at 6- and 12-month follow-up interviews. In contrast to these positive findings, in a large longitudinal health services study Zemore and Kaskutas (2008) found that having a sponsor at 2, 4, and 8 weeks after presentation for treatment did not increase the odds of complete abstinence at 6-month follow-up, with abstinence defined as the 30-day period before the 6-month interview. Consistent with this negative finding, in a 1-year naturalistic single-group design of inner-city drug injection users, Crape, Latkin, Laris, and Knowlton (2002) reported that although TS attendance was strongly predictive of complete abstinence at 1-year follow-up (24.2% versus 48.7% abstinent), having a TS sponsor was unrelated to abstinence.
This paper aims to investigate the direct and specific effects of AA sponsorship on later substance use. In so doing, this study will address many of the methodological ambiguities associated in prior work on AA sponsorship. First, variables of interest will be temporally and logically sequenced to both minimize issues of measurement covariation as well as clarify the impact, if any, of AA sponsorship on later substance use. Second, to isolate the relationship of interest a comprehensive set of variables reflecting several dimensions of AA participation, formal help seeking, and motivation will be used as covariates. Third, continuous daily drinking and illicit drug use data will be used to define abstinence, hence avoiding the need to infer that a subset of days adjacent to the interview represents behavior throughout the entire assessment window. Fourth, and related, four measures of substance use outcome will be considered to adequately and sensitively detect potential effects.
Method Participants
This study was conducted in the context of a larger prospective study investigating AA-related behavior change. The parent study recruited 253 alcohol dependent adults from community-based AA (n = 68) and as they presented for outpatient substance abuse treatment (n = 185). Eligibility criteria were narrow in terms of lifetime and recent treatment and AA experiences to investigate how substance abusers mobilize and sustain behavior change in AA, unconfounded by prior change histories. Thus, prospective participants were excluded if they reported more than 16 weeks of lifetime AA exposure and/or if they reported having successfully achieved abstinence for 12 months or longer after they had first determined their substance use to have become a problem. To be included, participants had to meet current Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM–IV; American Psychiatric Association) criteria for at least alcohol abuse, consumed alcohol in the prior 90 days, and attended at least one AA meeting in the prior 3 months. Illicit drug abuse and dependence were not exclusion criteria.
Procedures
Breathalyzers were used to ensure that a participant's blood alcohol concentration (BAC) did not exceed .05 prior to the consent process or at any of the subsequent interviews. Once consented, participants were administered a baseline interview that included 15 self-report questionnaires, three semi-structured interviews, and urine toxicology screens for five classes of illicit drugs. Follow-up interviews were conducted in 3-month increments for the first year (e.g., 3, 6, 9, and 12). No intervention was offered in this assessment-only study although clinical referral could be made when requested by the participant or when deemed warranted by Center on Alcoholism, Substance Abuse, and Addictions (CASAA) clinical staff. Follow-up rates for the 3-, 6-, 9-, and 12-month interviews were 93.7, 94.1, 93.7, and 91.7%, respectively. Participants were compensated $50 for each completed interview. All procedures and assessments were approved by the institutional review board at the University of New Mexico (UNM Protocol No. 24028).
Assessments
Substance use
The Form 90 ( Miller, 1996) was used to gather calendar-based alcohol use and other drug use data, ideally collected in 90-day intervals. One reliability study (Tonigan, Miller, & Brown, 1997) indicated satisfactory self-report reliability on abstinent days from alcohol (r = .79 for outpatients and r = .97 for aftercare patients), heavy drinking use days (r = .96, and .97), and number of drinks per drinking day (r = .94, and .95), and a second test–retest study (Westerberg, Tonigan, & Miller, 1998) with polysubstance abusers, reported that the calendar-based procedure had good reliability on frequency of cocaine use days (r = .77) and marijuana use days (r = .80). Urine toxicology screens for five classes of illicit drugs were collected at intake and at the 3- and 24-month follow-up interview. The Syva Rapid Test is manufactured by Siemens Healthcare Diagnostics in Deerfield, IL. It employs a one-step solid phase immunoassay technology to rapidly and qualitatively detect the presence of THC, opiates, cocaine, PCP, and amphetamine. Cut-off ng/ml concentrations for the five drugs were: THC (50), opiates (300), cocaine (300), PCP (25), and amphetamine (1,000).
Four outcome measures of substance use were computed using the Form 90. Complete abstinence from alcohol was defined as no reported alcohol use between the 3- and 6-month interview (6-month outcome analyses: M number of days = 93.68, SD = 24.26) and no alcohol use between the 9- and 12-month interviews for the 12-month outcome analyses (M number of days = 102.35, SD = 41.86). A parallel definition was used for the 6- and 12-month analyses when determining complete abstinence from alcohol, marijuana, and cocaine. Proportion of days abstinent (PDA), the third outcome measure, was defined as the number of alcohol abstinent days in a period divided by the total number of days in the assessment period. Last, drinks per drinking day (DPDD) was defined as number of drinks consumed per drinking day divided by the number of drinking days in a period (abstinent days not included in the denominator).
Help-seeking behaviors
The Alcoholics Anonymous Involvement (AAI) questionnaire ( Tonigan, Connors, & Miller, 1996) was developed to assess AA program and fellowship behaviors and practices. Normative data have been published on the AAI, and test–retest psychometric analyses indicate that the AAI scales and items are reliable and valid. A single item from the AAI was used at each interview to identify which respondents currently had an AA sponsor (yes/no). The General Alcoholics Anonymous Tools of Recovery (GAATOR; Montgomery, Miller, & Tonigan, 1995) is a 24-item 4-point Likert scaled self-report survey that was developed to assess commitment to, and practice of, the TS of AA. Sample items include: “I have shared my personal inventory with someone I trust,” “I have made a list of my resentments,” and “I have prayed and meditated.” Items were evaluated on a scale from 1 to 4, with 1 indicating strong disagreement and 4 indicating strong agreement. Psychometric work suggests that the GAATOR has three scales that can be used separately or summed to yield a total score representing the practice of prescribed AA-related behaviors and beliefs (Tonigan, Miller, & Vick, 2000). Readiness for behavior change was measured using the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES; Miller & Tonigan, 1996), a self-report tool with 19 items that yields three scales: Ambivalence (four items), Problem Recognition (seven items), and Taking Steps (eight items). Problem Recognition and Taking Steps scales have demonstrated prognostic value in representing the positive role motivation serves in predicting later substance use (e.g., Miller & Tonigan, 2001), and these two scales were selected as covariates in an effort to further isolate the impact of AA sponsorship.
In addition to calendar-based drinking information, the Form 90 also collects frequency data on formal and informal health care utilization. Nonoverlapping number of day's treatment for alcohol, drug, and emotional problems were summed and divided by the total number of days (outpatient and inpatient) in the assessment period to derive a proportion of day's formal psychosocial treatment in an assessment period. Likewise, the number of days AA was attended in an assessment period was divided by the total number of days in an assessment window to compute proportion of days that AA was attended. This strategy is an effective and psychometrically sound method to use Form 90 frequency counts when the actual number of days in an assessment window varies across individuals ( Tonigan et al., 1997; Westerberg et al., 1998).
Statistical analysis
Four dependent measures were separately evaluated at both 6- and 12-month follow-up (two binary and two interval scaled). For the two binary outcomes (1) alcohol abstinence and (2) abstinence from alcohol, marijuana, and cocaine, hierarchical logistic regression analyses were conducted to assess the unique contributions of sponsorship (yes/no) on the binary outcome, with classification of sponsorship group determined by self-report (3 month for the 6-month analyses, and 9 month for the 12-month analyses). Prior to entering the dummy coded sponsorship variable in step two, in step one we entered total GAATOR score, two scales from the SOCRATES, proportion of days attending AA, proportion of days receiving treatment, and baseline PDA and DPDD. Important, the total GAATOR score, the two scales of the SOCRATES, and the proportion of days attending AA and/or treatment variables were for these time periods: the 3 months prior to beginning the study, from intake to 3 months, and from 4 to 6 months. The same strategy using hierarchical linear regression analysis was used for the two interval scaled outcomes, PDA and DPDD (6 and 12 months). Prior to analyses, PDA and DPDD were subjected to data transformations, arcsine and square root, respectively. As before, 17 covariates were entered in Block 1 and the sponsorship variable was entered in Block 2.
ResultsData from five different assessment periods were used to isolate the specific effect of sponsorship on four different substance abuse outcome measures. Seventy-two percent (n = 182) of the recruited sample had complete data. Table 1 provides key demographic and drinking variables for included and excluded participants. No significant differences were found between the two groups on any of the demographic or drinking variables. We find it interesting that men and women in the included group did not differ on any demographic variables except for unemployment. Men were significantly more likely to be unemployed, Pearson's χ2(1, N = 181) = 6.73, p < .01, κ = .193. Regarding substance use measures, the only significant gender difference was that men were higher in Problem Recognition relative to women, t(179) = 2.06, p < .05.
Characteristics of Included and Excluded Study Participants
Corroboration of Self-Report
High agreement was obtained between self-report and urine toxicology (UA) screens, with the frequently observed pattern that self-reported use identified a larger percentage of participants as illicit drug users. Specifically, at intake UAs indicated that one participant had used marijuana although the participant denied such use, and at 3 months, there were no contradictory reports of marijuana use. Noteworthy, UAs failed to identify 51 participants who reported marijuana use at intake and 21 participants who reported marijuana use at the 3-month interview. For cocaine, five participants were positive via the UA, although denying it at intake, and at 3 months, four participants provided positive UAs, although denying such use. In contrast, at intake 47 participants reported cocaine use who did not provide a positive UA, and at 3 months, 19 participants had negative UAs, although they reported cocaine use.
Substance Use and AA
Table 2 shows the substance use and AA participation of the sample for 12 months. Large reductions in the intensity of drinking were observed over the 12 months (d = −.98) and frequency of abstinent days increased about 25%. Slightly less than half of the participants reported complete abstinence from alcohol for Months 10 to 12 (41%). Forty-six percent of participants had an AA sponsor at intake, and approximately 40% reported having a sponsor at each follow-up assessment. Secondary analyses indicated that having a sponsor at 3 months was unrelated to participant gender (p < .42) and problem severity as measured by the Alcohol Dependence Scale, p < .46 (ADS; Skinner & Allen, 1982). Readiness for change measured by the Problem Recognition and Taking Steps scales of the SOCRATES at intake, was significantly related to having a sponsor at 3 months, r = .22, p < .01 and r = .25, p < .01. Seventy-two percent of those reporting having a sponsor at 3-months also had a sponsor at 9-months, and again, participant gender was unrelated with sponsorship at 3 and 6 months. A majority of the participants reported attending AA at each interview and, on average, they attended an AA meeting about once every 7 days throughout the course of the study, except for the first 3 months when AA was attended about twice a week.
Sample Drinking and AA Measures: Intake Through 12 Months
Proportion of AA attendance days (Months 0 to 3) was significantly predictive of each of the four substance use outcome measures (Months 4 to 6). Specifically, bivariate correlations between proportion AA days and (1) alcohol abstinence was r = .36, p < .001; (2) combined abstinence from alcohol, marijuana, and cocaine, r = .29, p < .001; (3) proportion of days abstinent, r = .40, p < .001; and (4) drinks per drinking day, r = −.18, p < .05. Use of the Q statistic indicated that the absolute difference in the magnitude of these four bivariate correlations did not exceed sampling error, Q(3) = 1.77, p < .62. Thus, the mean weighted bivariate correlation of rw = .28, 95% CI [.14. .41] is conceptually the most stable estimate of the magnitude of AA-related benefit at early follow-up in this study, an estimate that compares favorably with a prior meta-analytic estimate, for example, rw = .31 ( Emrick et al., 1993).
AA Sponsorship
Two hierarchical logistic regression and two hierarchical linear regression analyses were conducted to investigate the effect of sponsorship from Months 0 to 3 on substance use from Months 4 to 6. The first and second steps of the regression analyses were the same in all four analyses. To isolate the effect of sponsorship, known correlates of reduced substance use were added in the first step. The measures entered in Step 1 were intake PDA and DPDD and five additional covariates, each of which contributed three measures (collected at intake, 3-, and 6-month interviews). The covariates were proportion days AA attended, total GAATOR score, two SOCRATES scales, and proportion days any type of treatment. Sponsorship from Months 0 to 3 was entered in the second step for each regression analysis in predicting 4- to 6-month substance use.
Early AA Sponsorship
The first logistic regression investigated the effect of sponsorship (Months 0 to 3) on self-reported abstinence from alcohol (Months 4 to 6). Controlling for variables entered in Step 1, having a sponsor was significantly predictive of abstinence, β = 1.30, p < .01, odds ratio (OR) = 3.67, 95% CI [1.48, 9.13]. A second logistic regression employed a binary measure of self-reported abstinence from alcohol, marijuana, and cocaine. Again, sponsorship was a significant predictor after first controlling for a host of AA-related and substance use-related variables (Months 0 to 6), β = 1.16, p < .05, OR = 3.19, 95% CI [1.30, 7.82]. Summarized, having an AA sponsor (Months 0 to 3) increased the probability of complete abstinence at Months 4 to 6 nearly three-fold after first controlling for past and concurrent: AA, treatment, readiness for change, and intake drinking. Two hierarchical linear regressions were then conducted to assess the effect of sponsorship (Months 0 to 3) on the continuous measures of PDA and DPDD (Months 4 to 6). An arcsine transformation was applied to PDA and a square root transformation was used with DPDD. Having a sponsor made a significant and independent contribution to the prediction of PDA, standardized β = 0.19, p < .01, and DPDD, standardized β = −0.17, p < .05. With a Bonferroni correction to control for inflated Type I error (.05/4 = .0125), the three abstinence-based outcomes for Months 4 to 6 retained significance although the measure of drinking intensity, DPDD, failed to achieve statistical significance.
Later AA Sponsorship
An identical analytical strategy was used to assess if having a sponsor (Months 7 to 9) predicted substance use outcomes at Months 10 to 12. The same covariates were used, this time collected at Months 3 to 12. Intake PDA and DPDD were, as before, entered to control for prestudy drinking. Sponsorship at Months 7 to 9 was not predictive of any of the four substance use measures at the 12-month follow-up (smallest p < .12).
Post Hoc Analyses
Sponsorship involves encouragement to work the TS and the social support for achieving this objective. Independent t tests were done to see if sponsored and nonsponsored adults differed in the mean number of steps completed, with the completion of steps grouped as Surrender steps (1 to 3, score range 0 to 3), Action steps (4 to 9, score range 0 to 6), and Maintenance steps (10 to 12, score range 0 to 3). No mean differences in the number of steps completed in any of the three step categories were found at 6 months contingent on AA sponsor status at 3 months, smallest p value < .17. In contrast, AA sponsor status at 9 months was generally significantly related to step completion at 12-month follow-up. Here, adults with sponsors reported, on average, significantly higher rates of completing Surrender steps, t(96.76) = 2.91, p < .01, Maintenance steps, t(85.92) = 1.91, p < .06, and Action steps, t(107.62) = 2.02, p < .05.
DiscussionFindings offer strong support for the importance and benefits of acquiring an AA sponsor during early AA affiliation. Specifically, having an AA sponsor during early AA affiliation was significantly and positively predictive of later abstinence, regardless of whether abstinence did or did not consider the use of illicit drugs. Illustrating the advantage of having an AA sponsor, for instance, participants with sponsors at 3 months were almost three times as likely to be abstinent from alcohol at 6 months as AA-exposed adults who had not acquired an AA sponsor. Continuing, participants with sponsors at 3 months reported 21% more abstinent days (in a 90-day window) at the 6-month interview and, when drinking did occur, they reported drinking two drinks less than AA-exposed adults without a sponsor. Noteworthy, the benefits associated with sponsorship were found after first statistically controlling for a host of prior and concurrent variables that are associated with AA participation and that are also reported to be prognostic of outcome.
In contrast, no support was found for the unique value of AA sponsorship at 9 months in predicting 1-year abstinence on any of the outcome measures. How can we reconcile the pattern of our findings with the extant literature? In particular, studies have offered mixed conclusions about the benefit, if any, associated with AA sponsorship in the first 6 months of AA affiliation (e.g., Witbrodt & Kaskutas, 2005; Zemore & Kaskutas, 2008) but most studies have reported that, at 1 year and later, AA sponsorship was significantly associated with increased abstinence (e.g., Bond et al., 2003). Our findings suggested exactly the opposite. In part, we believe that different analytic strategies may account for this disparity in findings. Using a covariation approach, for example, in our study 52.1% of the adults with sponsors (at 12 months) also reported complete alcohol-abstinence at 12 months while only 32.7% of the adults without a sponsor reported abstinence, an important cross-sectional association. The lagged-based findings in this study, however, suggest that this association is simply that; adults in AA who remain abstinent at 12 months also tend to have sponsors more often than AA-exposed adults who do not remain abstinent. In this regard, AA sponsorship appears to be best considered an active ingredient with highest potency during initial efforts to engage in AA.
How can we explain the changing benefit of AA sponsorship? Speculating, it seems reasonable that AA affiliates with sponsors were more likely to have more social support for abstinence than were people without a sponsor, especially during early AA affiliation when group- and member-based social relationships were still developing. At 12 months, however, this relative advantage in social support may have become diluted as people without an AA sponsor became increasingly integrated within the AA social context. Encouragement to work the TS, the second role of an AA sponsor, is a less plausible explanation for study findings. Specifically, when sponsorship was associated with increased step work (later follow-up) this did not become manifest in differential improvement. Oppositely, when no difference was found in completed step work between sponsored and nonsponsored groups (early follow-up) the sponsored group reported higher rates of abstinence.
Several points deserve attention. First, overall pre–post reductions in substance use in this sample were substantial and they were observed across three commonly abused drugs; alcohol, marijuana, and cocaine. Illustrating these improvements, pre–post effect size estimates for increased days abstinence (d = .77) and reduced drinks per drinking episode (d = −.98) were large by most standards, for example, Cohen (1988). Second, at 6 months, 37% of the participants reported alcohol abstinence and, of these adults, 30% reported abstinence from alcohol, marijuana, and cocaine. This discrepancy in overall sample abstinence rates with and without consideration of illicit drugs was found at all follow-up interviews. Although this study did not assess whether illicit drug use exceeded clinical thresholds of abuse and/or dependence, findings do suggest that alcohol abstinence does not necessarily imply abstinence from marijuana and cocaine. In this regard, we recommend that AA-focused researchers cast a broad net when assessing TS-related benefit. Third, contrary to our expectation the percentage of participants in this study with an AA sponsor remained relatively stable over 12 months. Unlike prior work that reported rapid decay in rates of AA sponsorship over a 12-month interval, we found that 41% of the sample reported having a sponsor at both the 3- and 9-month interviews. The reasons why the rate of AA sponsorship remained high in this study are unclear, but may relate to study inclusion criteria. In particular, study participants had, at most, only limited prior histories in AA and with formal treatment. In addition, at intake none of the participants reported prior success in achieving 1 or more years of abstinence once alcohol had been self-identified to be a problem. The sustaining of a sponsor may be less likely among adults with extensive prior AA histories. And, fourth, a majority of the participants at 9 months with a sponsor (72%, n = 48) also reported having a sponsor at 3 months. It therefore seems unlikely that the absence of an AA sponsor effect at 9 months was the result of examining the relationship of interest using participants that systematically differed from those with sponsors at the earlier follow-up. Also surprising was the relative stability in reported practice of the TS as measured by the GAATOR over time, a finding that may be associated with the relatively stable use of AA sponsors.
Some study limitations should be noted. Foremost, although the role of the AA sponsor is relatively clear there is wide variability in how sponsorship is actually structured and experienced. This study did not assess the frequency of sponsor contact and/or sponsee progress through the TS, for example, nor did we assess the extent that individuals perceived how and why sponsors were helpful (or not). We suspect that the direct effect identified between those who did and did not have a sponsor in this study may be influenced by the nature and practice of the sponsor–sponsee relationship. Second, the four outcome measures used in this study were correlated, sometimes highly (e.g., two binary abstinence measures, r = .84 at 6 months). This situation exacerbates the multiple-comparison problem, for example, inflated Type I error. Noteworthy, however, with Bonferroni adjustment to control for inflated Type I error three of the four inferential tests conducted at the 6-month period retained statistical significance. Third, excluded participants did not appreciably differ from those who provided sufficient information to be included in this study. At intake, the two largest observed differences centered on frequency of marijuana use and one scale on the readiness for change measure. On average, included participants reported lower values on both of these measures although the absolute magnitude of these differences was deemed modest and not statistically significant. Nevertheless, the possibility remains that unintended biases were introduced through study selection criteria.
In sum, acquiring an AA sponsor is highly encouraged within AA, and TS therapy frequently uses evidence-based strategies to facilitate the acquisition of an AA sponsor while clients are still in treatment. Findings suggest that these recommendations and practices are justified, especially immediately after treatment when relapse rates are highest. A stronger case can be made that abstinence-related benefit associated with having a sponsor in early AA affiliation is the result of focused social support, but this inference lacks empirical support at this time.
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Submitted: October 19, 2009 Revised: January 13, 2010 Accepted: January 17, 2010
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Source: Psychology of Addictive Behaviors. Vol. 24. (3), Sep, 2010 pp. 397-403)
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Record: 33- Language-based measures of mindfulness: Initial validity and clinical utility. Collins, Susan E.; Chawla, Neharika; Hsu, Sharon H.; Grow, Joel; Otto, Jacqueline M.; Marlatt, G. Alan; Psychology of Addictive Behaviors, Vol 23(4), Dec, 2009 pp. 743-749. Publisher: American Psychological Association; [Journal Article] Abstract: This study examined relationships among language use, mindfulness, and substance-use treatment outcomes in the context of an efficacy trial of mindfulness-based relapse prevention (MBRP) for adults with alcohol and other drug use (AOD) disorders. An expert panel generated two categories of mindfulness language (ML) describing the mindfulness state and the more encompassing 'mindfulness journey,' which included words describing challenges of developing a mindfulness practice. MBRP participants (n = 48) completed baseline sociodemographic and AOD measures, and participated in the 8-week MBRP program. AOD data were collected during the 4-month follow-up. A word count program assessed the frequency of ML and other linguistic markers in participants’ responses to open-ended questions about their postintervention impressions of mindfulness practice and MBRP. Findings supported concurrent validity of ML categories: ML words appeared more frequently in the MBRP manual compared to the 12-step Big Book. Further, ML categories correlated with other linguistic variables related to the mindfulness construct. Finally, predictive validity was supported: greater use of ML predicted fewer AOD use days during the 4-month follow-up. This study provided initial support for ML as a valid, clinically useful mindfulness measure. If future studies replicate these findings, ML could be used in conjunction with self-report to provide a more complete picture of the mindfulness experience. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Language-Based Measures of Mindfulness: Initial Validity and Clinical Utility
By: Susan E. Collins
University of Washington;
Neharika Chawla
University of Washington
Sharon H. Hsu
University of Washington
Joel Grow
University of Washington
Jacqueline M. Otto
University of Washington
G. Alan Marlatt
University of Washington
Acknowledgement: This research was supported by National Institute on Drug Abuse Grant R21 DA010562 to G. Alan Marlatt. Susan E. Collins’s time was supported by an NIAAA Institutional Training Grant (T32AA007455) awarded to Mary E. Larimer at the University of Washington.
We would like to thank our research assistants, Benjamin Ady and Scott Henry, for their help entering and cleaning the data for this study. Thanks also to Anne Douglass, Katie Witkiewitz, Ph.D., Sarah Bowen, Ph.D., and Michelle Garner, Ph.D., for their input during the mindfulness language discussion round.
Mindfulness-based relapse prevention (MBRP; Witkiewitz et al., 2005) is an 8-week substance-use aftercare program that integrates mindfulness practice with cognitive-behavioral relapse prevention (Daley & Marlatt, 2006; Marlatt & Gordon, 1985). In a recent randomized trial comparing the efficacy of MBRP to treatment as usual (TAU), MBRP significantly reduced rates of substance use and craving during the 4-month follow-up (Bowen, Chawla, Collins, et al., in press). Relative to TAU participants, MBRP participants exhibited significant increases in acceptance and ability to act with awareness. Additionally, high participant satisfaction and treatment compliance demonstrated the feasibility of the MBRP program. Thus, findings supported MBRP as an efficacious and feasible aftercare treatment for substance use disorders.
A logical next-step is to evaluate potential correlates of the observed MBRP treatment effects. According to Shapiro, Carlson, Astin, and Freedman (2006), self-report measures may help determine whether posttreatment increases in mindfulness are correlated with better health outcomes. However, the language individuals use to describe their experience of mindfulness may also serve as a behavioral indicator of the development of mindfulness. In fact, the ability to describe observed phenomena by applying words comprises one component of mindfulness as it is currently defined (Baer, Smith, Hopkins, Krietemeyer, & Toney, 2004). This ability may also be indicative of a “de-centered” or “metacognitive” perspective, which has been proposed to be an important mechanism by which mindfulness affects outcomes (Teasdale et al., 2002). Additionally, facilitators of mindfulness-based interventions are trained to model qualities such as acceptance and being in the present moment, which is aided by the careful and intentional use of language.
On the other hand, the compatibility of literal language and mindfulness has been disputed. It has been asserted that verbal processing undermines acceptance and attention to the present moment because it activates a concrete and learned “relational network” of meanings and labels (Hayes & Shenk, 2004; Hayes & Wilson, 2003). Despite these differing assertions, no studies to date have empirically examined the relationship between language use and mindfulness, which indicates a gap in the literature that should be addressed.
One strategy for examining language and assessing its role in treatment outcomes is the use of word count programs, such as the Linguistic Inquiry and Word Count program (LIWC2007; Pennebaker, Booth, & Francis, 2007). The LIWC locates and counts the occurrence of words contained in writing samples that represent linguistic and psychological categories. The LIWC issued from research exploring the impact of expressive writing on physical and psychological health (Pennebaker, 1997; Pennebaker, Kiecolt-Glaser, & Glaser, 1988), but it has been more recently expanded to other psychological research areas, including how linguistic markers reflect psychological states (Pennebaker & Chung, 2007), social behavior (Mehl & Pennebaker, 2003; Pennebaker & Graybeal, 2001), and psychopathology (e.g., depression, suicidality; Rude, Gortner, & Pennebaker, 2004; Stirman & Pennebaker, 2001).
Although the LIWC has been used with adult substance-use populations (Pennebaker & King, 1999; Vano, 2002), only one study to date has applied this method to predict substance-use outcomes following an intervention (Collins, Carey, & Smyth, 2005). Specifically, college drinkers receiving personalized normative feedback versus an alcohol education brochure used more first person singular and school-related words and fewer discrepancy, second person and body-related words when describing their responses to the intervention. Use of first- and second-person pronouns, which typically reflects level of personal “ownership” and self versus other focus, mediated the intervention effects. Personalized normative feedback recipients appeared to connect with and internalize the intervention, and in turn, decreased their drinking more than participants who received the alcohol education brochure. This study showed that word-count analyses can be used to learn more about relevant language correlates and corresponding cognitive processes potentially underlying substance-use intervention effects.
Current Study: Aims and HypothesesThis study examined relationships among language use, mindfulness, and substance-use outcomes in the context of an efficacy trial of MBRP for adults with substance-use disorders. An expert panel of mindfulness and substance-use researchers generated two categories of mindfulness language (ML) words believed to reflect the mindfulness experience. The first category describes the actual state of mindfulness; whereas the second category describes the more encompassing “mindfulness journey,” which includes words describing the mindfulness state as well as the challenges involved in developing a mindfulness practice. Next, the LIWC word count program assessed the frequency of ML encountered in participants’ responses to open-ended questions about their postintervention impressions of mindfulness practice and the MBRP program. Finally, concurrent, discriminant and predictive validity of these ML categories was tested by assessing their relationship to other linguistic categories, related text-based sources, and alcohol and other drug (AOD) use days (i.e., frequency of AOD use) during the 4-month follow-up.
It was hypothesized that the ML categories would demonstrate convergent validity by corresponding to substantive high-frequency words in the MBRP treatment manual. ML categories would also demonstrate discriminant validity by showing little overlap with the twelve-step, self-help manual, the “Big Book” (Alcoholics Anonymous, 2006), which comes from a different theoretical and philosophical tradition. It was also hypothesized that ML would correlate with related linguistic variables. Specifically, the focus of mindfulness practice on experiences in the present moment (e.g., Baer et al., 2006; Kabat-Zinn, 1994; Marlatt & Kristeller, 1999) led us to hypothesize that ML would positively correlate with use of the present tense and would inversely correlate with past and future tense. Further, considering the focus of mindfulness on examining one’s own internal states, we hypothesized that ML would positively and negatively correlate with use of personal versus impersonal pronouns, respectively. Given the focus of mindfulness practices on increasing awareness and perception of states and sensations in the body (Bowen, Chawla, & Marlatt, in press), we hypothesized a correlation with perception and body-related words. Because mindfulness teaches clients to accept negative emotions and engage in skillful rather than reactive behavior, we hypothesized relationships between ML, affect, and anger words. It was also hypothesized that ML would evince clinical relevance and predictive validity by inversely predicting number of AOD use days during the 4-month follow-up.
Method
Participants
Participants (n = 48) were drawn from the MBRP treatment group (n = 93) that was part of a larger randomized treatment trial (n = 168) conducted in a nonprofit public service treatment agency (for details, see Bowen, Chawla, Collins et al., in press). The final subsample was reduced to 48 participants because only 52% of MBRP participants attended the final treatment session during which ML was assessed. Participants’ (27% women; n = 13) average age was 40.89 years (SD = 10.61). Most participants self-identified as White (69%); whereas 13% self-identified as Black, 8% as Hispanic/Latino, 8% as Multiracial, 6% as Native American, and 2% as Asian/Pacific Islander. Employment status varied: 33% were unemployed, 29% received public assistance or social security, 19% were employed part-time, and the remaining 17% were employed full-time. A high school degree or equivalent was the highest level of education for 31% of the sample; 4% did not complete high school, 42% completed community college or some 4-year college, and 21% obtained a 4-year college degree or higher.
Measures
A Sociodemographics Questionnaire assessed age, gender, race or ethnicity, employment status, and educational level. The Timeline Followback (TLFB; Sobell & Sobell, 1992) assessed daily AOD use and was used to create AOD use days (frequency) variables for the 2 months before baseline and during the 4-month follow-up. The TLFB has shown good reliability and validity for AOD use assessment (Carney et al., 1998; Fals-Stewart et al., 2000). The Participant Feedback Form included four open-ended questions that addressed participants’ experiences in the MBRP program and was administered at the final treatment session [e.g., “What did you get out of coming (to the MBRP group), if anything?”]. Responses to these items provided the writing samples used to assess ML use.
Materials
Individual text files were analyzed using the LIWC (Pennebaker, Booth et al., 2007), a computer program that categorizes words into 80 hierarchical dimensions, including linguistic, psychological, relativity-related, and other content categories. The LIWC created proportions of the number of words in each writing sample representing linguistic categories hypothesized to be significantly correlated with mindfulness (i.e., verb tense, pronoun use, affect, anger, insight, body, and perception). The linguistic categories are built into the default dictionary (i.e., LIWC 2007 English dictionary; Pennebaker, Booth et al., 2007) and have been researched and validated in various writing samples since the development of the program (Pennebaker, Chung et al., 2007). ML dictionary words were brainstormed and discussed by mindfulness experts, entered into two LIWC dictionaries, and counted by the LIWC program (see Table 1). Word frequency counts for the MBRP treatment manual (Bowen, Chawla, & Marlatt, in press), and the 12-step Big Book (Alcoholics Anonymous, 2006), were generated using custom software written in the Perl computer programming language. This program scanned the manuals and sorted words from most to least frequent. A separate table held “filtered” words (i.e., articles, pronouns, and prepositions) to be ignored when inspecting manual overlap.
Mindfulness Language Categories
Procedure
Near the end of their inpatient or intensive outpatient treatment, participants were recruited, screened and randomized into the larger treatment trial (see Bowen, Chawla, Collins, et al., in press). Participants presented at their treatment site to complete the baseline data assessment, including assessment of AOD and sociodemographic variables. Surveys were self-administered using computers set up to access the Web-based survey. Trained, undergraduate research assistants provided instruction and assistance during assessments as needed. Next, participants underwent either TAU or the 8-week, group-based MBRP treatment. The Participant Feedback Form was completed by MBRP participants who attended the final treatment week. Participants provided AOD use days data at the posttest, 2- and 4-month follow-ups, which were completed at the treatment facility. If participants did not complete scheduled follow-ups, substance use data were obtained via telephone. Participants received gift cards for completion of assessments.
Data Preparation
Trained undergraduate research assistants double entered data from the Participant Feedback Form into separate text files for each participant. Text files were cleaned by the first author and were run through the LIWC program using the LIWC 2007 default dictionary and the two ML dictionaries created for the current study. The program yielded the proportion of participants’ writing samples reflecting words from the chosen linguistic and ML categories. These data were entered into STATA 10 (StataCorp, 2007) for the analyses.
ResultsPreliminary analyses showed a significant association between race and use of mindfulness journey words (U = 68, p = .03); thus, race was entered as a covariate in later AOD use analyses. There were no other associations between baseline demographic variables and AOD use or ML (ps > .12). As predicted, ML words largely correlated with hypothesized linguistic markers (see Table 2). Convergent validity was further supported in that a word count conducted on the MBRP manual (nwords = 28,989) indicated that all ML words appeared at least twice (M = 62.98, SD = 74.40), and collectively made up 13.5% of the total manual words. Word frequency analyses established the 100 most frequently occurring words in the MBRP manual and 12-step Big Book. Aside from articles, pronouns and prepositions, there was only a 1-word overlap between ML and the 100 most frequent words in the Big Book (see Figure 1). A proportional z test indicated that overlap between ML and the MBRP treatment manual was significantly higher than the overlap between ML and the Big Book (z = 4.70, p < .001).
Bivariate Spearman’s Rho Correlations Between Mindfulness and Other Linguistic Categories
Figure 1. Mindfulness language (ML) words are in the center column. Words shaded in black represent overlap between ML and the 100 most frequent words in the MBRP manual (Bowen, Chawla, & Marlatt, in press), which account for 55.60% (n = 16,119) of the total words in the MBRP manual. Words shaded in gray represent overlap between ML and the 100 most frequent words in the Big Book (Alcoholics Anonymous, 2006), which account for 56.76% (n = 16428) of the total words in the Big Book.
Separate zero-inflated negative binomial models (ZINB; Cameron & Trivedi, 1998; Hardin & Hilbe, 2007) tested the association of ML and total number of AOD use days during the 4-month follow-up. The two, separate mindfulness state, χ2(3, n = 41) = 18.22, p = .0004, and journey, χ2(3, n = 41) = 8.15, p = .04, models both predicted AOD use days. After controlling for race and baseline AOD use days, there were inverse effects for mindfulness state (IRR = .03, SE = .05, p = .02) and journey (IRR = .55, SE = .13, p = .01) language. Thus, for each 1% increase in the use of ML in a given writing sample, the rate of AOD use during the 4-month follow-up was reduced by 97% and 45%, respectively. Neither mindfulness state nor journey words predicted the zero-inflation process (ps > .19).
DiscussionThis study examined the relationships among mindfulness language, linguistic markers, and substance-use treatment outcomes. Findings largely supported the convergent validity of the ML categories by confirming the hypothesized associations between ML and linguistic variables. ML was inversely related to participants’ use of past tense words, which reflects the focus of mindfulness practices on present moment experience (e.g., Baer et al., 2006; Kabat-Zinn, 1994; Marlatt & Kristeller, 1999). Greater use of ML was also associated with decreased use of impersonal pronouns, which may reflect the MBRP focus on examining one’s own internal states and supports the notion that mindfulness practice fosters a greater sense of agency and personal choice (Kabat-Zinn, 1990; Segal et al., 2002). Mindfulness state words were positively associated with use of affect and body-related words, which is consistent with the focus of mindfulness practices on increasing awareness of affective states and associated bodily sensations (Bowen, Chawla, & Marlatt, in press), and with neurobiological research showing that mindfulness meditation is associated with changes in areas of the brain involved in interoceptive and visceral awareness (Critchley et al., 2004; Holzel et al., 2008). The association of mindfulness journey words with fewer anger and more insight words also fits the focus of mindfulness practice on interrupting automatic and reactive behavior and helping participants develop skillful responses when confronted by triggering situations (Segal et al., 2002).
Convergent validity was further supported in the text-based analyses: high-frequency words in the MBRP manual resembled the ML categories in that they comprised more active, present tense verbs as well as tactile and sensory experience words. Further, 38% of the ML words were contained within the 100 most frequent words in the MBRP manual. The percent of content overlap between ML and the MBRP manual was significantly higher than the overlap between ML and the Big Book.
In support of the ML categories’ discriminant validity, only one word (“time”) in the mindfulness journey category appeared in the 100 most frequent words in the Big Book. The words were also qualitatively different: the Big Book list comprised less experiential and more concrete words (e.g., “had,” “not,” “drink,” “God,” “alcoholic”) than the ML and MBRP lists. This finding conformed to hypotheses and is not surprising given the philosophical differences between MBRP and 12-step approaches. Although there are several points of overlap between the two models, including emphasis on acceptance and the value of meditation (Hsu, Grow, & Marlatt, 2008), the philosophical underpinnings of 12-step approaches are based largely on the disease and spiritual models of addiction (Spicer, 1993). Affected individuals are encouraged to accept the label of an “addict” or “alcoholic,” and to enlist the support of a Higher Power to aid them in their recovery, which may explain the emphasis in the Big Book on words such as “alcoholic” and “God.” In contrast, MBRP discourages use of and identification with labels and encourages ongoing observation and acceptance of all thoughts, sensations, and emotional states (Bowen, Chawla, & Marlatt, in press; Marlatt, Bowen, Chawla, & Witkiewitz, 2008). This may explain the greater emphasis of the MBRP manual on tactile, sensory, and present tense verbs and its considerable overlap with ML.
ML use predicted AOD frequency during the 4-month follow-up period, which supported the predictive validity of ML. This finding also provided support for the hypothesized underlying process: that MBRP should increase mindfulness, which should in turn help participants decrease their AOD use. Unfortunately, because there was no baseline assessment of ML and because the writing samples were not available for participants in the control condition, it is impossible to ascribe causality to the relationship between mindfulness and later substance use. This finding did, however, provide evidence that level of mindfulness language is a valid and clinically relevant construct. Future experimental studies may explore whether change in ML over the course of the MBRP treatment mediates substance use behavior change.
Limitations of this study deserve mention. First, there was a relatively low rate of attendance at the final session, during which participants completed the writing samples used to assess ML. It is also important to note that the mindfulness state words are a subset of the mindfulness journey category; thus, the two categories are overlapping and highly correlated. Although it was deemed important to capture the subtle distinctions between the actual state of mindfulness versus the challenges inherent in mindfulness practice, the predictive models of AOD should not be interpreted independently without acknowledgment of this overlap. Finally, follow-up attrition was relatively high over the 4-month follow-up period. The resulting data missingness may have introduced bias into the dataset and reduced power to find significant differences (Kazdin, 1998). These flaws limit the conclusions that can be drawn; however, the robustness of the findings that ML is a valid and clinically relevant behavioral measure of mindfulness is encouraging.
Despite the limitations, ML showed potential as a valid and clinically relevant representation of mindfulness. ML was associated with other linguistic variables believed to represent key aspects of mindfulness, showed appropriate content overlap with relevant text-based sources, and predicted subsequent substance use outcomes. Future studies may use larger samples and experimental designs to further investigate ML as a valid and clinically useful way to assess mindfulness and as a potential mechanism underlying mindfulness-based treatment effects on substance use outcomes.
Footnotes 1 Zero-inflated negative binomial (ZINB) regression is a type of generalized linear models designed for count outcomes that are positively skewed, overdispersed (i.e., the variance is greater than the mean), and have a preponderance of zeroes (i.e., zero responses are more frequently observed than would be expected given the distribution). ZINB models two processes for each participant. The first process is a Bernoulli trial, which much like a logistic regression, determines the probability that an observation is consistently zero or is a feasible count response predicted by the negative binomial distribution. For example, if participants are abstinent from AOD use before treatment begin and remain so, they may never enter the count process because they are considered to be “always-zero” responses (Hardin & Hilbe, 2007). If the observation may be predicted by the negative binomial process, it enters this count estimation.Negative binomial regression provides output much like a multiple (OLS) regression, but the interpretation of the regression coefficients is different. Instead of standardized regression coefficients or betas, exponentiated coefficients or incident rate ratios (IRR) may be interpreted as the rate of change in the outcome variable for each one-point increase in the predictors. IRRs ranging from 0 to 1 indicate an inverse relationship between the predictor and outcome; whereas IRRs greater than 1 indicate a positive relationship between the predictor and outcome. There is no widely accepted statistic that provides a percentage of variance accounted for (R2). There are various pseudo-R2 statistics for generalized linear models more generally, but they may neither be interpreted as percent variance accounted for nor are they widely agreed upon (Hardin & Hilbe, 2007).
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Submitted: March 25, 2009 Revised: July 31, 2009 Accepted: August 4, 2009
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Source: Psychology of Addictive Behaviors. Vol. 23. (4), Dec, 2009 pp. 743-749)
Accession Number: 2009-24023-023
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Record: 34- Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data. Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 1031-1040. Publisher: American Psychological Association; [Journal Article] Abstract: Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Measurement Error and Outcome Distributions: Methodological Issues in Regression Analyses of Behavioral Coding Data
By: Tracy Holsclaw
Department of Statistics, University of California, Irvine
Kevin A. Hallgren
Department of Psychiatry and Behavioral Sciences, University of Washington;
Mark Steyvers
Department of Cognitive Sciences, University of California, Irvine
Padhraic Smyth
Department of Computer Science, University of California, Irvine
David C. Atkins
Department of Psychiatry and Behavioral Sciences, University of Washington
Acknowledgement: Tracy Holsclaw, Mark Steyvers, Padhraic Smyth, and David C. Atkins were supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant R01AA018673. Kevin A. Hallgren was supported by NIAAA Grant T32AA007455, and Theresa Moyers’s behavioral coding data was obtained with support from NIAAA Grant R01AA13696. We thank Theresa Moyers for the use of her data in this project.
As the literature on interventions for substance use disorders (SUD) has grown, research aims have shifted from a focus on testing treatment efficacy to identifying within-treatment processes and mechanisms that lead to reduced substance use (DiClemente, 2007; Doss, 2004; Kazdin & Nock, 2003; Longabaugh et al., 2005). Much of the research on mechanisms of change in SUD treatment has relied on behavioral coding to study within-session therapist and client behaviors, and the use of behavioral coding has led to some of our best data thus far on mechanisms of SUD treatment (e.g., see Magill et al., 2014).
Behavioral coding data have specific features that can violate the assumptions of regression models in psychology and addiction science. Specifically, common regression models assume that independent variables are measured without error, even though there can be a considerable degree of measurement error due to interrater disagreement when rating behaviors. As discussed in detail below, measurement error in independent variables can bias effect sizes and significance tests, leading to inaccurate results and conclusions. However, at present, measurement error is rarely accounted for in the statistical analyses of behavioral coding SUD research, despite the literature indicating that measurement error is notable and pervasive.
There appears to be limited awareness of the impact of measurement error in behavioral coding research and no consensus on methods for handling it statistically. In addition, behavioral coding data often violate other statistical assumptions, including the assumption that residuals are normally distributed and homoscedastic, since these data are often count- or proportion-based variables with substantial skew and heteroscedasticity. This issue has been discussed in the context of modeling alcohol consumption (e.g., Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013; Xie, Tao, McHugo, & Drake, 2013) but not within the context of behavioral coding data. Further, statistical analyses assume that the variables used for analysis (e.g., total counts of behaviors) are not confounded by other variables, but verbosity and session length can lead to confounding among count variables in coding data (Cohen, Cohen, West, & Aiken, 2003).
The current manuscript reviews these characteristics of behavioral coding data and their implications for statistical analyses using theory, simulation, and real-world data analyses. Particular attention is focused on measurement error in independent variables, which can substantially impact statistical analyses but has received minimal attention in SUD research. In addition to critiquing current practice, the present article presents an improved approach to regression analysis that utilizes (a) weighted regression to account for measurement error in predictor variables, (b) count regression to account for non normality in behavioral count data, and (c) offset terms and rates for covariates to account for differences in verbosity and session length.
Measurement Error Has Implications for Regression ModelsMotivational interviewing (MI) is an efficacious treatment for SUDs (Hettema, Steele, & Miller, 2005; Lundahl & Burke, 2009), with a strong tradition of corresponding behavioral coding research. MI has proposed clinical models of how client and therapist verbal behaviors relate to one another and in turn lead to changes in client substance use (Miller & Rollnick, 2012; Miller & Rose, 2009). Behavioral coding research has tested these models empirically (Miller, Benefield, & Tonigan, 1993); for example, finding that therapist MI-consistent behaviors, such as open questions, complex reflections (i.e., adding meaning to what a client has said), and complex reflections of change (i.e., a complex reflection focused on reasons or commitments to reduce substance use) increase client change talk (i.e., statements indicating desires, reasons, or commitments to reduce substance use) and reduce sustain talk (i.e., statements indicating desires, reasons, or commitments to continue substance use; Moyers et al., 2007; Moyers & Martin, 2006; Moyers, Martin, Houck, Christopher, & Tonigan, 2009; Vader, Walters, Prabhu, Houck, & Field, 2010).
Much of the support for theories of causal chains in MI has been developed through behavioral coding performed by human raters. Human raters do not always agree, and most behavioral coding studies employ multiple raters to code a common subset of sessions to quantify the degree of disagreement among raters. Researchers often take two practical steps for working with multiply rated sessions (i.e., sessions rated by two or more coders for the purpose of assessing reliability). First, interrater reliability is quantified for variables of interest, such as the frequencies of different codes, and only variables with good interrater reliability, determined using a predefined cutoff value (e.g., Cicchetti, 1994), are retained in model testing. Second, for sessions with two or more ratings, a single set of ratings from one coder is randomly selected to include in subsequent statistical analyses and other coders’ ratings are excluded in the main hypothesis testing (except hypotheses directly related to reliability analysis). Measurement error within the ratings is then given little or no further statistical consideration despite the implications of ignoring such error.
For a recent example, among the 16 studies in a meta-analysis of causal chain hypotheses in MI (Magill et al., 2014), most studies reported at least one behavioral coding variable with interrater reliability estimates that indicated only “fair” agreement (i.e., intraclass correlation coefficients [ICCs] between 0.40 and 0.60) or “poor” agreement (i.e., ICC <0.40). Moreover, there is notable variability in how measurement error is treated within analyses. For example, approximately half of the same 16 studies excluded variables with poor agreement from statistical analyses, while other studies retained all coding variables in hypothesis testing even when agreement was poor. Most studies acknowledged that measurement error could affect the accuracy of statistical models when agreement was poor but did not further comment on the precise manner in which the results would be affected. Further, there was no discussion of the impact of measurement error on the results when reliability was better than “poor” but still not perfect (i.e., between 0.40 and 1.00), even though measurement error was often substantial for such variables. Although a detailed review of measurement error in MI or psychotherapy coding research is beyond the scope of the present study, these practices indicate that measurement error is prevalent across most studies, is often given little attention, and is often handled differently across settings.
Standard regression models assume no measurement error in the independent variables (i.e., predictor variables), and thus, behavioral coding data often violate this assumption. An example of this issue and its potential consequences are presented in the left panel of Figure 1, which shows six different 95% confidence intervals (CI) for regression coefficients of change talk predicted by complex reflections. Each coefficient and CI was created by randomly selecting a single set of ratings from sessions with multiple ratings (between two and four), using data from an MI coding study (Moyers et al., 2009; a full description of the study and data are provided later in the paper). The measurement error inherent in the data is seen in the lack of consistency over the various random samples; if there were little to no measurement error, each point estimate and CI should be virtually identical. Each combination of data produces a different point estimate for the effect of change talk predicted by complex reflections of change. Four of the combinations of the data indicate a significant relationship between change talk and complex reflections of change, whereas two combinations of rater scores fail to find a significant association. The measurement error in the independent variable is illustrated in the right panel of Figure 1, which shows the ranges of counts for complex reflections of change (y-axis) provided by different coders (separate dots connected by lines) for each multiply rated session (x-axis).
Figure 1. Left panel: point estimates and 95% confidence intervals for regression coefficient estimates of change talk predicted by complex reflections of change. Each point estimate estimates the association between complex reflections of change and change talk using different subsets of individual coders’ ratings from sessions that were coded by multiple raters. Right panel: ranges of complex reflections of change identified in multiply rated sessions.
Although not commonly used in psychology or SUD research, appropriate methods for handling measurement error in regression have been developed and used in statistics and econometrics. These models are often referred to as error-in-variables (EIV) models (Fuller, 1987; Carroll, Ruppert, & Stefanski, 1995). This literature has demonstrated that measurement error in independent variables causes bias in regression and typically underestimates regression coefficients, suggesting that the strength of association is weaker than it truly is and increasing Type II error rates. This is directly related to the issue of attenuation due to measurement error in standard correlation coefficients (Carmichael & Coen, 2008; Cragg, 1994; Durbin, 1954). It is beyond the scope of the current article to give a full review of the EIV literature, and instead, we focus on one specific approach that is both appropriate for the types of behavioral coding data common to mechanisms-of-change research and straightforward to implement.
The approach we recommend is rooted in classical test theory (Lord & Novick, 1968). In psychometric measure development, each individual item is treated as an error-prone indicator of an underlying latent construct. Computing average values from two or more observations reduces the amount of random measurement error, producing a more accurate estimate of the true score for that session. As the number of observations of the same session increases, the averaged value generally moves closer to the true value, improving the accuracy of the estimate. Quite simply, taking an average over multiple ratings of the same (multiply coded) session is better than randomly selecting a single set of ratings. However, behavioral coding studies typically have a percentage of sessions that are multiply coded, and the remaining sessions are single coded. Thus, session scores that are averages of multiple raters will be more reliable than those that are from a single rater, because the former is averaging over measurement error whereas the latter is not. To incorporate the reliability from averaging, weighted regression can be used by giving the averaged ratings from multiply coded sessions more weight in the analysis than ratings of single-coded sessions. Moreover, using averaged ratings and weighted regression can easily extend to non normally distributed outcomes, which are also common with behavioral coding data and are the next focus of our discussion.
Count Data as Outcomes Are DifferentCommonly, behavioral coding variables reflect counts of different behaviors, such as sums of particular verbal utterances (e.g., simple reflections and open questions). Least squares linear regression models assume that residual errors of dependent variables are normally distributed and homoscedastic (i.e., constant across all fitted values). However, count data often violate this assumption because count data are bounded at zero and there is a direct relationship between the mean and variance with count variables, which typically produces heteroscedasticity and non normal residuals (Atkins & Gallop, 2007; Atkins et al., 2013; Hilbe, 2011; Gardner, Mulvey, & Shaw, 1995). For example, histograms of two common dependent variables in MI coding studies, change talk and sustain talk, are shown in Figure 2. The sustain talk count variable has a lower mean than the change talk variable and therefore has greater skew. However, both variables are positively skewed. Converting these variables into rates (i.e., dividing them by the total number of client utterances) also results in positively skewed variables.
Figure 2. Histograms of change talk (top row) and sustain talk (bottom row) using raw frequencies (left column) and rates (right column).
A common solution for reducing positive skew is to transform the outcome, for example, by using the square-root or natural log transformation (O’Hara & Kotze, 2010). However, these approaches often lead to biased regression coefficients and inefficient standard errors (Maindonald & Braun, 2007; King, 1988). Alternatively, the generalized linear modeling (GLM) framework provides a robust method for performing regression on discrete count data (Atkins & Gallop, 2007; Atkins et al., 2013; Hardin & Hilbe, 2012; Hilbe, 2011; Xie et al., 2013).
In GLMs, independent variables (e.g., therapist code counts) are connected to the dependent variable (e.g., count of change or sustain talk) through a link function, which guarantees that predictions from the model are in the allowable range. For example, the negative binomial regression model uses a log link function, which guarantees that predictions are never negative. Furthermore, as the name implies, the negative binomial regression model does not assume normally distributed outcomes, but instead, assumes that the distribution of the outcome, conditional on the included predictors (i.e., similar to residuals in least-squares linear regression models), is a negative binomial random variable. Combining weighted regression for multiply coded sessions with count regression for count outcomes, we can formulate a much more appropriate regression model for behavioral coding data. However, particularly with codes that are counts of utterances within a session, there is one final consideration.
Variability in Session Length and VerbosityMeasurements of behavior counts over a specific time period (e.g., the length of a session) can be affected by variability in the length of the measurement period (e.g., different session lengths) and by the overall number of utterances within the time period (e.g., due to different speech rates). For example, a therapist using five complex reflections during a 10-minute interview likely demonstrates higher quality MI than a therapist who uses five complex reflections during a 50-minute interview. For independent variables, a simple method for handling this issue is to convert the count covariates into rates by dividing them by the total number of utterances per session that were made by the speaker.
For count outcomes a similar conceptual approach is used, though the details are slightly different. GLMs for counts allow for an “offset” term, which in this case is simply a variable representing the length of exposure, such as the total number of client or therapist utterances in a session. The offset variable is typically defined as the natural log of the original exposure length variable. In psychotherapy coding studies, this variable will typically be (the natural log of) the total number of utterances made by the speaker. In the present data, session lengths (as measured by total number of utterances) varied notably across sessions (M = 479.8, SD = 130.1, range = 126 to 800; see Figure 3).
Figure 3. Variability in number of codes per session.
Failure to account for variability in exposure length can bias regression results. For example, longer sessions would likely have greater counts for all coding variables, and shorter sessions would likewise have fewer counts for all coding variables. This can cause different coding variables to appear more strongly associated with each other than they truly are because they are both mutual influenced by exposure length. Thus, to reduce the conflation of independent and dependent variables, we propose the following. First, use relative frequencies of behavior counts for independent variables, such as the proportion of behavior counts, which can be computed by dividing specific sums of each behavioral count by the total number of utterances made by the speaker. Second, use an offset term for dependent variables, such as the number of behavior codes in the full session for the client or therapist, which reduces the conflation between behavioral count frequencies and variability in length of the measurement period. Alternative measures of exposure could also be considered, such as the total amount of time that each speaker talks during a session; however, it is not common to measure per-speaker talk time in a session, and there is likely greater interest in controlling for the number of utterances in a particular session than in the amount of time it took to say them.
In summary, each of these three issues (measurement error, skewed count outcomes, and variable session length) can cause problems in the statistical analysis of behavioral coding data, including inaccurate standard errors, unreliable effect estimates, and inflated Type I and Type II errors. In general, this may increase the likelihood of obtaining misleading results and slow the progress of research on mechanisms of change in SUD treatments, potentially leading to misguided recommendations for therapists and treatment developers. These issues have often been neglected in existing behavioral coding studies, and the feasibility and results obtained using the recommended techniques have not been compared to methods that are typically used. Using both actual coding data as well as simulations, we explore these comparisons below.
Comparison of Statistical Models Data
For the present study, we use behavioral coding data from 119 first-session tapes of Motivational Enhancement Therapy, a treatment protocol based on MI, from five Project MATCH sites (Project MATCH Research Group, 1997). These data were coded, analyzed, and reported in previous mechanisms of change research (Martin, Christopher, Houck & Moyers, 2011; Moyers et al., 2009). Client and therapist behaviors were rated by six trained coders using the SCOPE coding instrument (Martin, Moyers, Houck, Christopher, & Miller, 2005). The SCOPE provides total frequency counts of client and therapist behaviors, and in the present study we focus only on two client codes, change talk and sustain talk, and two therapist codes, complex reflections and complex reflections of change, and only focus on total frequencies of these counts (i.e., not sequential coding).
Example 1: Normal Versus Poisson Versus Negative Binomial Regression
How do normal, Poisson, and negative binomial regressions compare to each other when modeling behavioral coding data? Two methods were used to compare regression models with different outcome distributions. First, deviance statistics (i.e., −2 times the log-likelihood) compared the fit of each model to the observed data. Second, a model-prediction version of deviance compared each model’s fit to new observations using a 10-fold cross-validation procedure (James, Witten, Hastie, & Tibshirani, 2013). Specifically, the data were subdivided into 10 equal parts and a model was fit on 90% of the data. The resulting model was then fit to the remaining 10% of the data, and this process was repeated nine more times to predict outcomes for each of the 10 subsets. As shown in Table 1, the negative binomial regression model had lower deviance (i.e., better fit to the data) and lower model-prediction deviance (i.e., better predictive performance) than the normal and Poisson regression models. This was especially the case for sustain talk, which had a heavier skew.
Average-Weighted Regression for Change Talk and Sustain Talk for Different Distributions
Example 2: Negative Binomial Regression With Varying Number of Raters
What effect does including multiple ratings of the same session have on parameter estimates? Using simulated data, we next show that there is a reduction of bias in parameter estimates when including multiple rater information in the presence of measurement error (i.e., nonperfect agreement among raters). Data were generated from a negative binomial distribution with measurement error in the dependent and independent variables. Independent variables were created to reflect different coder ratings with values that ranged from 1 to 10. A dependent variable was then simulated from a negative binomial distribution with a dispersion parameter of 10 and a “true” regression coefficient of 0.5, which defined the relationship between the mean of the independent variables (i.e., the averaged coder ratings) and the dependent variable. Normally distributed error variance was added to the independent variable ratings to represent measurement error, which was manipulated at three levels to produce interrater agreement ICCs approximately equal to 0.8, 0.6, and 0.4, corresponding to excellent, good-to-fair, and fair-to-poor agreement (Cicchetti, 1994) and representing values that are commonly obtained in MI coding studies. The latter of these values is often used as a cutoff point for inclusion in statistical analyses, although variables with ICCs below this have also been included in analyses in many MI coding studies.
Five hundred data sets were simulated under six study design scenarios. The first scenario included 100 sessions that were coded by only one rater (i.e., no multiply rated sessions). The second scenario included 100 sessions that were coded by two raters. The third, fourth, and fifth scenarios included 100 sessions that were coded by three, four, and eight raters, respectively. The sixth scenario created a data set that was similar to the real SCOPE data described above, with 70% coded by one rater and 30% coded by three raters (i.e., 60 duplicate ratings). No specific rater bias was included (i.e., all measurement error had a mean of zero).
In the analysis, all scenarios with multiply rated sessions used weighted regression, using averages of the multiple ratings. In scenario six, heavier weights were given to sessions that were based on averages such that the values of the weights were equal to the number of ratings used for computing the average. The first scenario (with only one rater) most closely represents coding studies with multiple raters in which a single set of ratings are randomly selected for the final analysis.
Table 2 shows the mean of the estimated regression coefficients across each condition. This example shows how the additional rater information can be included to reduce bias, shrink uncertainty, and increase precision in the parameter estimates. As additional ratings of the same sessions are included using averaging and weighting, the overall bias is reduced as represented by mean coefficient estimates that are closer to their true parameter value (i.e., 0.5). In scenarios with fewer overlapping ratings, the direction of the bias tended to systematically underestimate the true coefficient value. For example, when only one rater’s codes were used (Scenario 1) and reliability ICCs were 0.8, indicating “excellent” reliability (Cicchetti, 1994), mean coefficients underestimated the true relationship of 0.50 as 0.40. But when two raters’ codes were used (Scenario 2), the mean coefficient improved to 0.45, and when eight raters’ codes were used (Scenario 5), mean coefficients improved to 0.48. These effects were stronger as reliability worsened to 0.6 and 0.4, when the use of a single rater underestimated the same coefficient as 0.29 and 0.19, respectively. Using weighted regression with two raters improved these estimates to 0.37 and 0.28, and using eight raters improved the estimates to 0.46 and 0.42. In all cases, the uncertainty (i.e., lack of precision in parameter estimates) also decreased as the number of raters increased, as represented by a decrease in standard deviations of coefficient estimates. Each of these phenomena are well-known and expected within EIV research (Carmichael & Coen, 2008; Cragg, 1994; Durbin, 1954). Finally, although the weighted regression approach with typical behavioral coding data (i.e., final row of Table 2) is superior to randomly selecting a single rater (i.e., first row), there is still bias and inefficiency even with this approach. The degree of bias and inefficiency are related to a variety of factors (e.g., degree of measurement error), but as expected, more ratings will lead to more accurate parameter estimation.
Bias and Uncertainty of Regression Coefficients for Different Numbers of Raters
Example 3: Regression With Variable Exposure Lengths
What kinds of problems can arise when predictor and outcome variables are both untransformed count variables? In this example, we demonstrate that the use of raw frequencies (i.e., total behavior counts) as both independent variables and dependent variables can lead to inflated regression parameter estimates and increased Type I error rates when exposure lengths are not constant. Data are again simulated to emulate the distributions in the SCOPE data described above for 119 sessions where the total number of codes per session varied randomly with a mean of 490 and standard deviation of 132. Then, rates of therapist reflections and client change talk were each randomly assigned to account for anywhere from 10% to 25% of the utterances observed within the session. Importantly, the rates for reflections and change talk were sampled independently and were therefore uncorrelated; thus, nonzero regression parameter estimates for change talk predicted by reflections would indicate a spurious relationship between these variables. Regression models were tested with and without an offset parameter for the dependent variable and by using either rates or raw frequencies of behavior codes for the independent variable, and the procedure was repeated 1,000 times.
Histograms of parameter estimates for client change talk predicted by therapist reflections are presented in Figure 4. Models that omitted an offset parameter and used therapist behavior counts instead of rates (top-left panel of Figure 4) produced parameter estimates that were substantially positively biased, indicated by all regression coefficients being greater than zero despite the null relationship between rates of change talk and reflections that generated the data. In contrast, models that used an offset parameter (bottom-left panel of Figure 4), used rates instead of raw counts for the independent variable (top-right panel of Figure 4), or used both an offset parameter and rates of the independent variable (bottom-right panel of Figure 4) produced parameter estimates that were centered around the true value of zero.
Figure 4. Simulated regression coefficient estimates for change talk predicted by therapist reflections. Data were generated using a true coefficient of zero (no relationship between change talk and therapist reflections).
In this case, the raw counts for the independent and dependent variables are both directly influenced by the total number of utterances within a session because longer sessions are likely to have a greater number of both codes and shorter sessions are likely to have a smaller number of both codes. The use of an offset parameter and converting independent variables from frequencies into rates eliminates the variables’ shared overlap with session length, producing results that more accurately capture the true null relationship between variables.
Model Comparisons Using Real-World SCOPE Data
Finally, we demonstrate the use and interpretation of weighted negative binomial regression models by applying them to the real-world SCOPE data described above. First, a traditional linear regression model is estimated to predict client change talk from therapist complex reflections. The linear regression model does not account for error in independent variables, count outcome distributions, or variability in exposure. In addition, for multiply coded sessions, a single coder’s ratings are randomly selected while the remaining ratings are discarded. The results, presented as Model 1a in Table 3, show that complex reflections are associated with change talk with an unstandardized regression coefficient of 0.65, p < .001, indicating that an increase of one therapist complex reflection corresponds with an expected increase of 0.65 client change talk statements. However, identically structured models that select different single-coder ratings from the multiply coded sessions are presented as Models 1b and 1c, each providing different regression coefficient estimates, 0.56 and 0.58, and different standard errors, t test statistics, and p values, p < .001 and p = .005.
Real-World Regression Model Results
Next, weighted linear regression models are estimated in Model 2 that account for measurement error in independent variables by using averaging and weighting approach described above. The weighted linear regression model yields a regression coefficient of 0.80, p < .001, which is larger than each of the regression coefficients that were obtained using only single ratings. The standard error for the weighted linear regression model is similar to the standard errors provided by the nonweighted linear regression models, yielding larger z test statistics, lower p values, and, therefore, greater statistical power.
In Model 3, a weighted negative binomial regression model is tested that accounts for the count distribution of the outcome variable. The model also indicates an association between complex reflections and change talk with a coefficient of 0.015, p < .001, which is markedly different than the coefficients found in Models 1a–1c and Model 2, in part because it is on a natural log scale instead of a linear scale. The negative binomial regression provides the expected estimate of the natural log of the count dependent variable, which for a model with one predictor variable can be written as
where E(ln(yi)) is the expected value of the natural log of the dependent variable for observation i, β0 and β1 are regression coefficients for the intercept and independent variable found in the regression results, and xi is the observed value for the independent variable for observation i. The raw coefficients in negative binomial regression models (e.g., β1 above) are typically exponentiated (i.e., raised to the base e) and referred to as rate ratios (RR). In the present example, the RR is 1.015 and interpreted in the following way: For each one point increase in complex reflections, the mean of change talk increases by 1.5%. A one count increase in complex reflections, from 17.66 (the mean in the sample) to 18.66 would lead to expected (natural logs of) change talk of
and
When exponentiated, this indicates an expected increase by 0.85 (e4.036 – e4.021). This result is quite close to the regression coefficient from the weighted linear model, but note that the negative binomial regression is changing nonlinearly with the independent variable due to the dependent variable being predicted on a log scale (see Atkins et al., 2013 or Hilbe, 2011 for more detail).
In Model 4, an offset term is added to control for the variability in exposure lengths by entering the log of the total number of client utterances as an offset term, testing a final model that accounts for each of the three concerns raised in this paper. This yields a nonsignificant association between change talk and complex reflections with a regression coefficient estimate of −0.0005, p = .847, suggesting that the rates of change talk (rather than the raw frequencies) are not predicted by the number of complex reflections in a session. Likewise, Model 5 transforms the independent variable, therapist reflections, into rates by dividing the complex reflection frequencies by the total number of therapist utterances and still finds a nonsignificant association with a regression coefficient estimate of 0.67, p = .333. What appeared to be almost a one-for-one association between change talk and complex reflections in earlier models may actually have been confounded by a mutual dependence of both variables on session length.
In Model 6, a weighted negative binomial regression with an offset term is estimated that is nearly identical to Model 6, but the independent variable is replaced by therapist complex reflections of change talk rather than complex reflections more broadly defined. This alternative test is modeled after theories of MI, which have increasingly posited that therapists have a better chance at eliciting change talk if they specifically target change-related content in their reflections. A positive and significant association is found between change talk and complex reflections of change with a regression coefficient estimate of 0.020, p < .001. This suggests that although complex reflections, broadly defined, were unrelated to change talk (Model 4), a more specific form of complex reflections focused on change talk was associated with client change talk.
ConclusionsThe present article presents weighted negative binomial regression with an offset term as a preferred method for testing relationships among behavioral coding variables. The examples presented here and in previous econometrics research show that this regression technique improves the accuracy and precision of effect estimates. These issues are particularly salient for behavioral coding research, where measurement error is prominent, exists to varying degrees between codes and studies, and is often handled differently between studies. Although coding studies often employ methods to remedy poor interrater agreement (e.g., retraining or replacement of coders, revising coding manuals), low agreement may not always be avoidable, and even small amounts of measurement error can systematically bias results.
Researchers are at risk of reduced power and greater bias in regression coefficients, leading to greater risk of Type I and Type II errors, when there is measurement error in predictors, count-variable outcomes, and variability in exposure length. This was found in the real-world examples with the SCOPE data, in which the use of nonweighted linear regression reduced parameter coefficient estimates, produced greater deviance, and found a relationship between therapist complex reflections and client change talk that appeared to be confounded by speaker verbosity. The nonsignificant results in Model 4 of the example section were, in fact, useful findings, which indicated that complex reflections, defined as a therapist statement that adds meaning to what a client has said, appear unlikely to elicit client change talk. If traditional regression techniques were used (i.e., Models 1a–1c), it would be tempting to accept the positive association between complex reflections and change talk as evidence supporting theories of MI, which posit that therapist MI-consistent behaviors, which include complex reflections, may influence client change talk. However, the results found by using weighted negative binomial regression with an offset term revealed that change talk was unrelated to complex reflections (Models 4–5), but was related to complex reflections of change talk (Model 6), leading to a different, and likely more accurate, recommendation that therapists use complex reflections of change rather than complex reflections in general to elicit change talk.
Although these regression models have features that may be unfamiliar to some (e.g., negative binomial distribution, log-link function, regression weights, and offset term), we believe that most researchers will be able to grasp these models both conceptually and practically. These models can be implemented with a few simple lines of code in many statistical software packages. R and SPSS syntax is provided in the online supplemental materials to this paper, which may provide a starting point for using these models in practice. We encourage researchers who analyze behavioral coding data to gain familiarity with these models and use them with behavioral coding data.
Footnotes 1 Given the widespread use of structural equation models (SEM) in psychology that allow for measurement models in regression path models, readers may wonder about the current recommendation for averaging multiple ratings in a weighted regression framework. Some EIV models do estimate measurement error in independent variables, and thus, bear some similarities to SEM. However, these parametric EIV models are challenging (if not impossible) to use due to several features of behavioral coding data. Specifically, there are typically multiple ratings on the outcome (e.g., change talk) as well as the independent variables, which would necessitate a type of random effects EIV model. Moreover, as we describe later, behavioral coding data used as outcomes are often skewed, leading to non normal regression models, and finally, the number of sessions with multiple ratings are typically small (e.g., 10% or 20% of total number of sessions). These features (i.e., random-effects, non normal outcomes, small percentage of multiply rated data) present a formidable challenge to reliable and precise statistical estimation via SEM.
2 Most generally, we can think of an exposure variable as the denominator for our rate, whether time or duration, or area (e.g., total population in a given locale).
3 The natural log of the exposure variable is used because of the log link function. It can be shown that including an offset term serves to change the count outcome to a rate per unit of the exposure variable. Importantly, this is not equivalent to dividing the count outcome by the exposure variable in the raw data. See Hilbe (2011) for a thorough discussion.
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Submitted: October 6, 2014 Revised: March 31, 2015 Accepted: April 1, 2015
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Record: 35- New approaches for examining associations with latent categorical variables: Applications to substance abuse and aggression. Feingold, Alan; Tiberio, Stacey S.; Capaldi, Deborah M.; Psychology of Addictive Behaviors, Vol 28(1), Mar, 2014 pp. 257-267. Publisher: American Psychological Association; [Journal Article] Abstract: Assessments of substance use behaviors often include categorical variables that are frequently related to other measures using logistic regression or chi-square analysis. When the categorical variable is latent (e.g., extracted from a latent class analysis [LCA]), classification of observations is often used to create an observed nominal variable from the latent one for use in a subsequent analysis. However, recent simulation studies have found that this classical 3-step analysis championed by the pioneers of LCA produces underestimates of the associations of latent classes with other variables. Two preferable but underused alternatives for examining such linkages—each of which is most appropriate under certain conditions—are (a) 3-step analysis, which corrects the underestimation bias of the classical approach, and (b) 1-step analysis. The purpose of this article is to dissuade researchers from conducting classical 3-step analysis and to promote the use of the 2 newer approaches that are described and compared. In addition, the applications of these newer models—for use when the independent, the dependent, or both categorical variables are latent—are illustrated through substantive analyses relating classes of substance abusers to classes of intimate partner aggressors. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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New Approaches for Examining Associations With Latent Categorical Variables: Applications to Substance Abuse and Aggression
By: Alan Feingold
Oregon Social Learning Center, Eugene, Oregon;
Stacey S. Tiberio
Oregon Social Learning Center, Eugene, Oregon
Deborah M. Capaldi
Oregon Social Learning Center, Eugene, Oregon
Acknowledgement: This project was supported by awards from National Institutes of Health (NIH) Grant RC1DA028344 from the National Institute of Drug Abuse (NIDA), Grant R01AA018669 from the National Institute of Alcoholism and Alcohol Abuse (NIAAA), and Grant R01HD46364 from the National Institute of Child Health and Human Development (NICHD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, NIDA, NIAAA, or NICHD. We thank Isaac Washburn for his input on the Mplus syntax.
Categorical variables are frequently used in research in the behavioral sciences, especially in the addictions field. The nature of categorical data is that observations fall into discrete groups and the analysis examines group membership (Agresti, 2002), including proportion of respondents in a given category (e.g., prevalence of a substance use disorder) and probability of different patients attaining clinical goals (e.g., completing treatment). These variables may be composed using psychological indicators that define a behavioral taxonomy (e.g., Moffitt, 1993; sometimes called a typology; see Jackson, Sher, & Wood, 2000) developed from clinical observations or psychological theory. An alcohol use disorder (AUD), for example, is a clinically derived taxonomy that categorizes respondents into three mutually exclusive and exhaustive groups—for alcohol dependence, alcohol abuse, and no AUD—according to diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000). Examples of theory-driven taxonomies include batterer typology models of intimate partner violence (IPV; e.g., Holtzworth-Munroe, 2000; Holtzworth-Munroe, Meehan, Stuart, Herron, & Rehman, 2000; see also Capaldi & Kim, 2007, for a critique of those models).
Randomized clinical trials often use categorical outcomes (especially binary variables, such as alcohol consumption vs. abstinence) to determine treatment efficacy. Treatment completion is also an example of a dichotomous outcome measure often considered in program evaluations.
Although the distributions of categorical variables are of primary interest to epidemiologists studying the prevalence rates of disorders, psychologists generally are more concerned with examining associations between independent and dependent variables to test theories and hypotheses about human behavior. When only the independent variable is categorical (as is generally the case with experiments, including randomized clinical trials), dummy variable or other coding schemes can be used to capture the variable. The multiple regression analysis then proceeds exactly as if the variable was continuous, and is mathematically equivalent to analysis of variance (Cohen, Cohen, West, & Aiken, 2003).
When the dependent variable is nominal, however, categorical analysis methods, including chi-square and logistic regression analyses, are needed. The latter examines logit-transformed probabilities of falling into different outcome categories conditional on the independent variables (Hosmer & Lemeshow, 2000). When the outcome is dichotomous, binary logistic regression analysis may be used to compare the probabilities of being in one group (e.g., the treatment completion group) across the levels of the independent variable (e.g., treatment A vs. treatment B). A commonly used effect size for this difference in probabilities is the odds ratio (OR; Fleiss & Berlin, 2009; Haddock, Rindskopf, & Shadish, 1998), which is the categorical analogue of Cohen’s d for group differences in continuous outcomes (Feingold, 2009).
When the outcome measure consists of more than two unordered categories, multinomial logistic regression analysis (MLRA) can be used. In MLRA, one group is designated the reference category, and the analysis compares the probability of an observation belonging to each nonreference group relative with that of the reference group across levels of the independent variable. (The reference category in this case is very similar to the role of the reference class in dummy variable coding of categorical predictors in linear regression.)
Consider, for example, a prevention study examining whether a hypothesized childhood risk factor (e.g., family history of mental illness) predicts diagnosis of an AUD in adulthood. Given an adult AUD outcome with three categories (abuse, dependence, or neither), MLRA could be used to determine whether a family history of mental illness (a binary variable) increases children’s risk of succumbing to an AUD in adulthood. Adults who do not have a diagnosis of an AUD would serve as the reference category; one OR would convey the difference in the probability of the respondent being in the alcohol dependence category versus the AUD-free category (as a function of family history status); a second OR would be obtained for the corresponding difference in the probability of being in the alcohol abuse category versus the AUD-free category.
The categorical outcomes described above are examples of observed variables. There has been an increasing use of latent categorical variables, which capture unobserved heterogeneity within a sample, to study addictive behaviors (e.g., B. O. Muthén, 2006). Latent class analysis (LCA) is a popular technique that can be used to identify homogeneous subsamples of respondents from item endorsement patterns (Collins & Lanza, 2010; B. O. Muthén, 2008). When groups (called classes) are derived from a statistical analysis of item response patterns instead of measured directly, the categorical variable is latent rather than observed.
B. O. Muthén (2006), for example, conducted LCA of symptoms of alcohol dependence and alcohol abuse among current drinkers and identified four classes of people. The identification of subpopulations from such item response patterns should be distinguished from the classification approach taken by the psychiatric committees that developed diagnostic criteria for addictive disorders based on clinical observations. LCA has also recently been used to identify distinct subgroups of perpetrators of IPV (Ansara & Hindin, 2010; Carbone-Lopez, Kruttschnitt, & Macmillan, 2006; Klostermann, Mignone, & Chen, 2009).
A common method traditionally used to examine associations involving a latent categorical dependent variable is classical three-step analysis, which uses the posterior probabilities from an LCA to classify observations (e.g., people) on the basis of their likely class membership to create an observed variable that can serve as an outcome in a subsequent logistic regression or chi-square analysis (e.g., Agrawal, Lynskey, Madden, Bucholz, & Heath, 2007; Bornovalova, Levy, Gratz, & Lejuez, 2010). However, simulation studies have consistently found that classical three-step analysis underestimates the strength of associations between latent classes and observed covariates (Bakk, 2011; Bolck, Croon, & Hagenaars, 2004; Vermunt, 2010).
Alternatively, one-step analysis extracts latent classes and examines the association between latent categorical and observed variables simultaneously via a general latent variable modeling framework (e.g., B. O. Muthén & Shedden, 1999). As shown in Table 1, different one-step models are used when the independent, the dependent, or both variables are latent: (a) LCA with covariates (LCA-C)—also known as latent class regression analysis (e.g., Bandeen-Roche, Miglioretti, Zeger, & Rathouz, 1997)—predicts a latent categorical dependent variable (classes) from an observed variable (Goodman, 1974; Hagenaars, 1993); (b) LCA with distal outcomes (LCA-D) predicts an observed variable from a latent one (Asparouhov & Muthén, 2013); and (c) structural equation modeling with categorical variables (categorical SEM; Skrondal & Rabe-Hesketh, 2005) is used to predict one latent categorical variable from another. In these models (which can be thought of as logistic regression with latent variables), one or more ORs are obtained that capture the associations between the two categorical variables.
Overview of One- and Three-Step Models for Examining Associations With Categorical Variables
Unfortunately, one-step analysis is sometimes problematic, such as when large numbers of covariates are used in an exploratory analysis (Vermunt, 2010) or an observed covariate has a bimodal distribution (Asparouhov & Muthén, 2013). Bolck et al. (2004) were among the pioneers of corrected three-step analysis (referred to as three-step analysis in the contemporary literature) to relate a latent class variable to an observed covariate when one-step analysis is not ideal (or the investigator wants to ensure that the formation of the latent classes is not influenced by the observed variables to which the classes are to be related).
Although the improved three-step methods developed by Bolck et al. (2004) were not as efficient as one-step analysis, enhancements to them were introduced shortly thereafter (Clark & Muthén, 2009; Mplus Technical Appendixes, 2010; Wang, Brown, & Bandeen-Roche, 2005). Further refinements have resulted in methods of three-step analysis that are almost as efficient as one-step analysis in many cases (Asparouhov & Muthén, 2013; Vermunt, 2010).
These newest three-step procedures calculate the LCA first; the most likely class memberships are then obtained from the posterior probabilities of the LCA along with the classification uncertainty rate (i.e., measurement error); and the most likely class membership variables are then analyzed together with covariates or distal outcomes accounting for the measurement error in classification. However, Asparouhov and Muthén’s (2013) simulation studies identified conditions in which one-step analysis remains more efficient than three-step analysis. If, for example, low separation exists between the classes (i.e., entropy < .60) in one-step analysis, the covariates can influence class assignment, resulting in more separation between the classes and a more efficient estimation procedure compared with the three-step analysis. Thus, both one-step and three-step analysis methods need to be in the armamentarium of investigators working with categorical data, whereas the classical three-step approach was an interim solution that has largely outlived its usefulness.
The main purpose of this article is to promote one-step and three-step categorical modeling of categorical data as preferable alternatives to a classical three-step analysis that uses posterior probabilities from a latent variable model to create observed variables that can be included in a logistic regression or chi-square analysis. In addition to the explications of these different procedures for linking latent categorical variables to other categorical or continuous variables, worked examples of these models that relate classes of substance abusers to classes of perpetrators of IPV are provided.
Method Participants
The participants were drawn from a sample of 206 men who were enrolled in a long-term longitudinal study, the Oregon Youth Study (OYS)/Couples Study (Capaldi & Clark, 1998; Feingold, Kerr, & Capaldi, 2008), when they were in Grade 4 from schools with a higher incidence of delinquency in the neighborhood of a medium-sized city in the Pacific Northwest. The men were evaluated annually or biannually over 2 decades. In addition, data were obtained from the men’s romantic partners in several biannual waves that began once the OYS men entered their late teens. The present study analyzed data collected from the men when they were in their mid-20s (M = 25.9 years, SD = 0.7) and assessed for (a) lifetime DSM–IV–TR symptoms of substance dependence and abuse for different substances and (b) a history of perpetration of aggression toward partners who had participated with them in the Couples Study. The majority of participants (89.6%) were White. At time of last data collection, 30.7% were married and 22.8% were cohabiting.
Measurements and Procedure
Substance use symptoms
Substance abuse/dependence symptoms were examined in 199 men who were administered a structured psychiatric interview—Version 2.0 of the Composite International Diagnostic Interview (CIDI; World Health Organization, 1997)—that assesses lifetime DSM–IV–TR symptoms of dependence and abuse for each of nine psychoactive substances (nicotine, alcohol, cannabis, cocaine, opiates, hallucinogens, sedatives, amphetamines, and “other drugs”—although abuse of nicotine is not measured). The CIDI was used to determine whether participants had ever experienced at least one symptom of dependence and at least one symptom of abuse for each substance.
Five dichotomous variables were first created by assigning participants scores of 1 if they had ever had experienced one or more symptoms of dependence for nicotine, alcohol, and marijuana and one or more symptoms of abuse for alcohol and marijuana (else = 0). Second, two items for other drugs were created. Participants were assigned a value of 1 for other drug dependence if they had ever experienced a symptom of dependence on one or more of the CIDI-examined substances other than nicotine, alcohol, and marijuana (else = 0), and a value of 1 for other drug abuse if they had reported one or more symptoms of abuse for any substance other than alcohol and marijuana (else = 0). Thus, seven dichotomous items tapping dependence on nicotine, alcohol, marijuana, and other drugs and abuse of alcohol, marijuana, and other drugs were created from the CIDI.
Aggression
Commencing with the second wave of data in which men participated with romantic partners, the men’s partners at each time reported men’s aggression via four physical and five psychological aggression items on the Conflict Tactics Scales (CTS; Straus, 1979). There were up to three waves of CTS data collected from each man’s partner or partners by the time he had reached his mid-20s, which were examined once with the CIDI. For each of the 185 men who had participated with a romantic partner at one or more of these waves, partners’ CTS reports of victimization were used to determine whether he had perpetrated aggression during the year prior to each assessment period.
Because the CTS uses Likert scaling, never was scored 0 and other categories were recoded to 1. Next, for each CTS item, a score of 1 was assigned if the man was reported to have ever committed that act of aggression during at least one of the years before he was examined with a partner (else = 0).
Design and Analysis
The analyses began by extracting two independent sets of two-, three-, four-, and five-class solutions—one from the LCA of the substance dependence/abuse items and the other from the LCA of the aggression items. Two statistics obtained with LCA, the Bayesian information criterion (BIC) and the bootstrap likelihood ratio test (BLRT), have been found to be the most effective at identifying the number of latent classes that should be extracted from the indicator variables (Nylund, Asparouhov, & Muthén, 2007), and both criteria were considered in selecting from among the four different LCA solutions for each of the two constructs. With the BIC, the solution with the smallest value is identified as the optimal model, whereas the BLRT tests the statistical significance of the improvement in the model when an additional class is extracted.
Next, separate observed categorical variables were created for substance use and IPV from the posterior probabilities associated with the chosen solutions from the respective LCAs. To illustrate the use of classical three-step analysis, we first determined the associations between the two LCA-derived variables with an MLRA. The now-observed substance problems groups (the predictor variable) were dummy coded in the familiar manner used with nominal independent variables in ordinary least squares multiple regression (Cohen et al., 2003). One aggressor group (nonaggressors) was designated as the reference category, as a reference category is required for MLRA (Hosmer & Lemeshow, 2000). The MLRA thus yielded ORs for predicting the probability of being in an aggressor class (vs. the nonaggressor reference class) as a function of being in a substance abuse class (vs. a reference class of nonabusers).
Given that the two categorical variables were both latent, one- or three-step categorical SEM should be used to establish associations between them. However, to illustrate use of LCA-C and LCA-D, we used the posterior probabilities from one of the two variables to create an observed variable that was assumed for didactic purposes to be a naturally occurring observed categorical variable (e.g., gender). In each of the models that associated an “observed” categorical variable with a latent one (one- and three-step LCA-C and one-step LCA-D), we included one of the two LCA-derived variables created for use in the prior MLRA in the analysis as observed and treated the other variable as latent. Thus, the LCA-Cs were LCAs of the aggression items that included as a covariate the groups derived from the LCA of the substance-related symptoms (and assumed for pedagogical purpose to be an observed nominal variable). The one-step LCA-D was an LCA of the substance use items, with the aggressor groups derived from the LCA of the aggression items included as the observed categorical distal outcome. Finally, one- and three-step categorical SEM analyses, which examined both substance use symptoms and aggression as latent categorical variables, were used to establish the linkages between the two constructs by regressing the latter on the former.
ResultsAll analyses were conducted using Mplus (Aspourhov & Muthén, 2013; L. K. Muthén & B. O. Muthén, 2012). Appendix A contains the Mplus input statements used to conduct the one-step analyses (LCA-C, LCA-D, and categorical SEM) and Appendix B contains commands for conducting the three-step analyses (LCA-C and categorical SEM).
LCA of Substance Use Symptoms
In the LCA of the substance abuse/dependence items, the BIC was smallest for the three-class solution and the BLRT was statistically significant for the three-class solution but not for the four-class solution (see Table 2), both of which indicated that three classes—with about an equal number of men in each class (see Table 3)—should be extracted. The entropy for the three-class solution was .85, indicating that the men could adequately be assigned to substance abuse classes. The top panel in Table 3 reports the structure matrix for this solution.
Bayesian Information Criterion Values and Bootstrap Likelihood Ratio Tests for Latent Class Analysis of Substance Abuse Symptoms and Conflict Tactics Scales (Aggression) Items
Item Endorsement Probabilities From Three-Class Solutions From Latent Class Analysis of Substance Use Problems (n = 199) and the Conflict Tactics Scales (n = 185)
The men in the first class (the drug problem-free class) generally never had a DSM–IV–TR symptom of substance dependence or substance abuse, although a significant minority (nearly one third) had experienced at least one symptom of nicotine dependence during their lifetimes. All the men in the second class (the soft drug-abusing class) had a history of symptoms related to alcohol problems but a notable minority also reported symptoms for marijuana dependence or abuse and more than one half had been dependent on nicotine. However, almost none of the men in this class had experienced problems with hard drugs. The men in the remaining class (the polysubstance-abusing class) generally had a history of symptoms associated with abuse of and dependence on nicotine, alcohol, marijuana, and hard drugs.
LCA of Aggression Items
The BIC for the three-class LCA solution for the aggression items was smaller than the BIC found for either of the other two solutions, indicating three classes should be extracted (see Table 2). The BLRT, however, was statistically significant for the four- but not the five-class solution, indicating that four rather than three classes should be extracted. However, the four-class solution extracted two similar classes of men with a history of perpetration of both physical and psychological aggression (called global aggressors), suggesting that the three-class solution should be accepted. The entropy for the three-class solution was .83, indicating that participants could adequately be assigned to IPV classes. The bottom panel in Table 3 reports the structure matrix for the three-class LCA solution for the aggression items.
The first IPV class consisted of nonaggressive men, although a small majority of the members in that class had engaged in the mildest form of psychological aggression (sulking). The second, and largest class (which included almost one half of the sample), was composed of men who had perpetrated psychologically aggressive behaviors but had not physically harmed (or threatened to harm) at least one of their partners. The final, and smallest class (accounting for about one fifth of the sample), was composed of global aggressors.
Associating Latent Classes of Substance Abusers and Aggressors
MLRA
The classical three-step analysis (MLRA following LCA) was used to determine whether the probabilities of being in each of the two aggressor groups versus the nonaggressive group differed as a function of being in either of the two substance-abusing groups versus being in the drug problem-free group. Thus, two logistic functions were obtained for predicting (a) the probability of being a psychological aggressor versus a nonaggressor and (b) the probability of being a global aggressor versus a nonaggressor from the LCA-derived substance use categories. Because the substance use variable was composed of three groups, two dummy-coded variables were used to capture it. To form the first variable, we assigned a score of 1 to each participant if he was in the soft drug-abusing group and 0 otherwise. For the second variable, a score of 1 was assigned if he was in the polysubstance-abusing group and 0 otherwise. With two logistic functions and two dummy-coded variables as predictors, the analyses yielded four independent ORs for the probability of being in either of the two aggressor groups versus the nonaggressive group as a function of being in either of the two drug problem groups versus the drug problem-free group.
As shown in the top row of Table 4, the ORs for the differences in predicted probabilities obtained using MLRA indicated that male polysubstance abusers were more likely than men with no history of drug problems to be assigned to each of the two classes of aggressors than to the nonaggressor class. Men with a history of problems limited to abuse of soft drugs, however, were not more likely than men without any history of symptoms of substance abuse to be assigned to either aggressor class than to the nonaggressor class.
Regression of Aggressor Classes on Substance Abuser Classes by Analytic Method
LCA-C
In both the one- and three-step LCA-Cs, the dummy-coded groups derived from the LCA of substance use items that were previously used in the MLRA were now treated as observed covariates used to predict the latent classes of aggressors from groups of substance abusers. The structure matrix of the three-class solution of the aggression items from the one-step LCA-C was compared with the one obtained previously from the LCA conducted without the substance abuse groups included as a covariate (see Table 3 for the latter). None of the coefficients (item probabilities) for the aggression items from the LCA-C differed by more than .01 from respective coefficients obtained with the LCA that had not included a substance abuse variable as a covariate. Because LCA-C is conceptually equivalent to an MLRA with a latent covariate, the analyses produced ORs that reflected the same hypotheses of associations tested in the previous (MLRA) analysis. As predicted from the simulation studies that showed a downward bias in associations obtained with classical three-step analysis, the respective significant ORs were larger when obtained with LCA-C than with MLRA (see Table 4).
LCA-D
In the one-step LCA-D, the classes from LCA of substance use-related items were used to predict groups of aggressors. The structure matrix of the three-class solution of the substance abuse/dependence items from this analyses was compared with the one obtained previously from the LCA of the substance use items conducted without the aggression variable included as a distal outcome (see Table 3 for the latter). None of the coefficients (item probabilities) for the substance use symptoms from the LCA-D differed by more than .01 from respective coefficients obtained with the LCA that had not included aggression as a distal outcome. The two ORs obtained using the one-step LCA-D indicated the same associations found in the previous analyses but were expectedly larger than corresponding values obtained with MLRA that had treated both variables as observed.
Categorical SEM
Finally, one- and three-step categorical SEM analyses that specified three classes for each variable (with the numbers of classes predetermined from the LCAs that did not include auxiliary variables) were used to link the substance abuse and aggression classes. The item probabilities were also compared across classes for each of the two variables obtained from the one-step categorical SEM with the corresponding coefficients found in the separate LCAs of the same variables. In the analyses of the aggression items, the coefficients (item probabilities) were virtually identical (i.e., never differing by more than .01) in the LCA and categorical SEM, but that was not always the case with the substance use symptoms items. Comparisons between the two sets of coefficients indicated differences not exceeding .01 for 19 of the 21 item probabilities and a negligible .03 (.54 vs. .57) for another item. However, the probability of having an alcohol dependence symptom for those in the drug problem-free class was notably higher (.14 vs. .08) in the one-step categorical SEM than the LCA of substance use symptoms. The significance tests of the ORs from the one- and three-step categorical analyses yielded the same results obtained previously in the MLRA, LCA-C, and LCA-D analyses (see Table 4). The respective significant ORs, however, were found to be the largest in the one-step categorical SEM analysis, which were the only associations to attain significance at the .01 level.
DiscussionClassical three-step analysis is the traditional approach used to examine the relationship between two variables when at least one of them is latent and categorical: The posterior probabilities from an LCA are used to classify individuals, thus forming groups that can be treated in a subsequent categorical analysis (e.g., MLRA) as an observed nominal variable. To illustrate this approach, LCA was first used to generate three classes of substance abusers and three classes of aggressors from two independent sets of dichotomous response (indicator) variables. Next, MLRA was used to determine the association between the two now-observed categorical variables. The analysis of this 3 × 3 contingency table found that male polysubstance abusers were significantly more likely to have committed IPV than men with no history of drug problems, but men who had abused alcohol and/or marijuana but not hard drugs were not significantly more likely to have perpetrated such violence than men who had no history of substance abuse.
However, recent simulation studies have documented that the classical three-step approach using classification and MLRA considerably underestimates the true associations between variables (e.g., Bolck et al., 2004), a weakness not found when latent categorical variables are duly included in a one-step analysis. Because the categorical substance abuse and aggression variables used in the present study are both latent, the appropriate one-step analysis for these data is categorical SEM, in which the latent aggression construct is regressed on the latent substance abuse construct. Categorical SEM is thus a categorical counterpart to ordinary SEM, with the latter extracting dimensions (i.e., factors) from indicators and regressing a continuous outcome factor on a predictor (exogenous) factor.
As expected from simulation studies, the results from the categorical SEM and the MLRA were similar, but the ORs for statistically significant associations were consistently larger in the one-step analysis. Moreover, the two significant ORs were each significant at the .01 level in the one-step analysis but only at the .05 level in the MLRA.
To illustrate the application of two additional (and more common) types of one-step analyses of categorical data (LCA-C and LCA-D), we pretended that the LCA-derived observed group of either substance abusers or aggressors was a true nominally scaled variable and then conducted an LCA-C and an LCA-D, with substance abuse treated as a covariate in the former and aggression as a distal outcome in the latter. Both analyses yielded the same two significant associations between substance abuse and aggression classes found in the prior analyses. As expected, the magnitude of respective ORs obtained with LCA-C/LCA-D was somewhere in between values generated from MLRA and categorical SEM because one rather than neither or both of the variables was appropriately modeled as latent in LCA-C and LCA-D.
Modern methods of three-step analysis that are refinements of classical three step-analysis have recently been introduced and in some cases are preferable to the more established one-step analysis when calculating associations with latent categorical variables (Asparouhov & Muthén, 2013; Vermunt, 2010). These new three-step analyses are free from the strong downward bias in estimates characteristic of the prevalent classical three-step approach and have an advantage over one-step analysis in that the formation of the latent classes is not influenced by the observed covariate or the distal outcome (Asparouhov & Muthén, 2013; Vermunt, 2010). Accordingly, we also ran three-step LCA-C to afford comparisons with MLRA and one-step LCA-C. (Unfortunately, current statistical programs do not conduct a three-step LCA-D with an observed categorical distal outcome, precluding its illustration with our data.)
As expected, ORs for significant associations from the three-step LCA-C were larger than ORs obtained with classical three-step analysis (i.e., using MLRA) but smaller than ORs found with one-step LCA-C. Note that the findings from simulation studies of a slightly downward bias in the three-step analysis and a slight upward bias in the one-step approach (Vermunt, 2010) would mean the latter would be expected to yield larger estimates than would the former, which was indeed the case in our illustrative analyses of the two types of LCA-C.
Comparison of results from MLRA with one- and three-step categorical SEM found the same pattern of estimates observed in the previous analyses: The respective ORs were the largest in one-step analysis and smallest in MLRA.
Limitations
Because of this article’s focus on LCA with auxiliary variables, familiarity with the fundamentals of LCA (but not with methods for relating latent categorical variables to auxiliary variables) was assumed. Thus, many important issues regarding LCA were evaded, precluding use of this article as a comprehensive primer on it. These issues pertain to item selection, number of items to be used (and its relation to the maximum number of latent classes that can be observed), base rates of endorsement of the items, choosing from competing solutions when different criteria suggest different numbers of classes should be extracted, and requisite sample size for different kinds of analyses. However, there are numerous introductions to LCA (e.g., Bartholomew, Knott, & Moustaki, 2011; Collins & Lanza, 2010; Hagenaars & McCutcheon, 2002) that address these basic issues and that can be used in conjunction with this article by investigators new to categorical data analysis who want to apply these new methods to test their hypotheses.
Applications to Longitudinal Analysis
Although we have discussed modeling of cross-sectional categorical data, latent classes may also be derived from longitudinal analyses, such as growth mixture modeling (GMM) of alcohol use (e.g., Capaldi, Feingold, Kim, Yoerger, & Washburn, 2013; Sher, Jackson, & Steinley, 2011). Whereas LCA derives classes from multiple responses collected at a single time, GMM extracts classes from the same typically continuous outcome measured over time and defined by differences in trajectories of outcomes (L. K. Muthén & B. O. Muthén, 2012).
Both one-step and three-step approaches to LCA can also be used to examine associations involving classes obtained with GMM (Asparouhov & Muthén, 2013; L. K. Muthén & B. O. Muthén, 2012). Thus, categorical modeling can be used to link GMM classes to an observed variable through either GMM with covariates or GMM with distal outcomes, which correspond to LCA-C and LCA-D, respectively. Categorical SEM also can be used to examine associations (a) between independent and dependent latent categorical variables when both sets of classes have been extracted using outcomes that have been measured repeatedly and (b) between classes extracted from cross-sectional analysis and trajectory classes. For example, analysis of a continuous outcome measured over time could be used to extract latent trajectory classes to be predicted from latent classes formed from baseline item responses tapping, say, risk factors. The results would then address whether unobserved heterogeneity in risk characteristics at study onset predicts future growth in problematic behaviors. Finally, covariates and distal outcomes in LCA and GMM with auxiliary variables are not limited to categorical variables but may be observed or latent continuous variables (e.g., Guo & Wall, 2006).
Continuous Indicator Variables
Although LCA is not appropriate for examination of indicator variables that are continuous, latent profile analysis (LPA; Lazarsfeld & Henry, 1968) is an LCA analogue for use with continuous indicators. Mplus can be used to conduct one-step LPA-C and LPA-D that correspond to LCA-C and LCA-D, respectively. Three-step approaches for handling latent classes derived from continuous indicators have recently been formulated (Gudicha & Vermunt, in press).
Conclusions
Although we are not contending that classes derived from latent class analysis are necessarily more meaningful than groups formed from observed variables (e.g., DSM–IV–TR diagnoses), one-step and three-step analyses are generally preferable to the prevailing classical three-step analysis because they yield less biased ORs. The type of LCA models that should be used depends on the scaling of the variables, as some categorical variables (e.g., gender, diagnosis) are inherently observed rather than latent. Thus, LCA-C, LCA-D, and categorical SEM (with one-step or three-step analysis) are all suitable for particular categorical analyses, but the widely used classical three-step analysis is to be avoided because it underestimates the true associations between latent classes and auxiliary variables.
Footnotes 1 In probability theory, the odds of an event occurring is the probability that the event occurs divided by the probability that the event does not occur (Agresti, 2002; Feingold, 2012). The odds of an event (e.g., perpetration of aggression) can be calculated separately for each of two groups (e.g., men with and without a substance use disorder) using the observed proportions, and the ratio of the odds between the two groups is the OR. For example, if 50% of men without a drug problem and 80% of men with such a problem commit an act of violence, the odds of a man without a drug problem aggressing are 0.50/0.50 = 1.00; for a man with a drug problem, the odds are 0.80/0.20 = 4.00. The OR for the prediction of aggression from drug problem status would then be 4.00.
2 Although LCA is increasing in popularity, cluster analysis is an older method that can also identify homogeneous subsamples based on item responses and has been widely used in addictions research over the past 2 decades. Cluster-analytic studies have typically uncovered two broad classes of addicted patients: a less severely impaired class and a more severely impaired class (Ehlers, Gilder, Gizer, & Wilhelmsen, 2009; Feingold, Ball, Kranzler, & Rounsaville, 1996; Kranzler et al., 2008; Zucker, Ellis, Fitzgerald, Bingham, & Sanford, 1996). The use of LCA in the same studies would likely have identified the same subpopulations of substance abusers.
3 Although the sample size for the illustrative analyses is small, we feel it is adequate for analyses that are primarily pedagogical than substantive, especially given that our models were found to have high entropy values and no clusters with few cases assigned to them.
4 Two CTS items that concerned knives and guns were never endorsed in our sample and thus could not be included in the LCA of aggressive behaviors.
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APPENDICES APPENDIX A: Mplus Input Statements for One-Step Categorical Analysis With Auxiliary Variables
TITLE: LCA-C: LCA of IPV indicators with
SUD group as covariate
DATA: FILE IS F:\Data.dat; ! file containing IPV indicators and
SUD group
VARIABLE:
NAMES ARE Family IPV1-IPV9 SU1-SU7 IPVClass Poly
Soft;
USEVARIABLES ARE IPV1-IPV9 Poly Soft;
CATEGORICAL = IPV1-IPV9; ! defining indicators as cate-
gorical variables
MISSING = ALL(-9999); ! defining missing data value
CLASSES = cIPV (3); ! defining latent class name and
number of classes
ANALYSIS:
TYPE = MIXTURE; ! requesting a mixture analysis
STARTS = 100 10; ! requesting 100 initial random starting
values and 10 final stage optimizations
MODEL: ! model specification follows
%OVERALL% ! defining effects are for all classes
cIPV ON Soft; ! regressing IPV indicators on soft drug
SUD group
cIPV ON Poly; ! regressing IPV indicators on poly substance
SUD group
OUTPUT:
CINTERVAL; ! requesting confidence intervals
TITLE: LCA-D: LCA of substance use indicators with
IPV group as distal outcome
DATA: FILE IS F:Data.dat; ! file containing
SUD indicators and IPV group
VARIABLE:
NAMES ARE Family IPV1-IPV9 SU1-SU7 IPVClass Poly
Soft;
USEVARIABLES ARE SU1-SU7 IPVClass;
CATEGORICAL = SU1-SU7; ! defining indicators as categor-
ical variables
NOMINAL = IPVClass; ! defining IPV class as a nominal
variable
MISSING = ALL(-9999); ! defining missing data value
CLASSES = cSU (3); ! defining latent class name and number
of classes
ANALYSIS:
TYPE = MIXTURE; ! requesting a mixture analysis
STARTS = 100 10; ! requesting 100 initial random starting
values and 10 final stage optimizations
MODEL: ! model specification follows
%cSU#1% ! defining below effects are for first SUD class only
[SU1$1 - SU7$1]; ! SUD indicators
[IPVClass#1] (IPV1SUD1m); ! class mean for IPV group 1,
SUD class 1
[IPVClass#2] (IPV2SUD1m); ! class mean for IPV group 2,
SUD class 1
%cSU#2% ! defining below effects are for second SUD class
only
[SU1$1 - SU7$1]; ! SUD indicators
[IPVClass#1] (IPV1SUD2m); ! class mean for IPV group 1,
SUD class 2
[IPVClass#2] (IPV2SUD2m); ! class mean for IPV group 2,
SUD class 2
%cSU#3% ! defining below effects are for third SUD
class only
[SU1$1 - SU7$1]; ! SUD indicators
[IPVClass#1] (IPV1SUD3m); ! class mean for IPV group 1,
SUD class 3
[IPVClass#2] (IPV2SUD3m); ! class mean for IPV group 2,
SUD class 3
MODEL CONSTRAINT: ! calculating log odds ratios and signif-
icance tests using difference between class means
NEW LogGLP LogPAP LogGLS LogPAS;
LogGLP = IPV1SUD1m - IPV1SUD3m;
LogPAP = IPV2SUD1m - IPV2SUD3m;
LogGLS = IPV1SUD2m - IPV1SUD3m;
LogPAS = IPV2SUD2m - IPV2SUD3m;
OUTPUT:
CINTERVAL; ! requesting confidence intervals
TITLE: C-SEM: Categorical SEM where SUD class predicts IPV
class
DATA: FILE IS F:\Data.dat; ! file containing IPV and SUD
indicators
VARIABLE:
NAMES ARE Family IPV1-IPV9 SU1-SU7 IPVClass Poly
Soft;
USEVARIABLES ARE IPV1-IPV9 SU1-SU7;
CATEGORICAL = IPV1-IPV9 SU1-SU7; ! defining indicators
as categorical variables
MISSING = ALL(999); ! defining missing data value
CLASSES = cSU (3) cIPV (3); ! defining latent class names
and number of classes
ANALYSIS:
TYPE = MIXTURE; ! requesting a mixture analysis
STARTS = 100 10; ! requesting 100 initial random starting
values and 10 final stage optimizations
MODEL: ! model specification follows
%OVERALL% ! defining effects are for all IPV and SUD
classes
cIPV ON cSU; ! regressing IPV class on SUD class
MODEL cIPV: ! defining LCA for IPV
%cIPV#1%
%cIPV#2%
%cIPV#3%
[IPV1$1-IPV9$1]; ! IPV indicators for all 3 IPV classes
MODEL cSU: ! defining LCA for SUD
%cSU#1%
%cSU#2%
%cSU#3%
[SU1$1-SU7$1]; ! SUD indicators for all 3 SUD classes
OUTPUT:
CINTERVAL; ! requesting confidence intervals
Note: Note. SUD = substance use disorder symptoms; IPV = intimate partner violence (aggression).
APPENDIX B: Mplus Input Statements for Three-Step Categorical Analyses With Auxiliary Variables
TITLE: Three-Step LCA-C: LCA of IPV indicators with
SUD group as covariate
DATA: FILE IS F:\Data.dat; ! file containing IPV indicators and
SUD group
VARIABLE:
NAMES ARE Family IPV1-IPV9 SU1-SU7 IPVClass Poly
Soft;
USEVARIABLES ARE IPV1-IPV9;
CATEGORICAL = IPV1-IPV9; ! defining indicators as cate-
gorical variables
MISSING = ALL(-9999); ! defining missing data value
CLASSES = cIPV (3); ! defining latent class name and number
of classes
AUXILIARY = Poly (r3step) Soft (r3step); ! defining covari-
ates using corrected 3-step method
ANALYSIS:
TYPE = MIXTURE; ! requesting a mixture analysis
STARTS = 100 10; ! requesting 100 initial random starting
values and 10 final stage optimizations
MODEL:
%OVERALL% ! defining effects are for all classes
[IPV1$1-IPV9$1]; ! IPV indicators
OUTPUT:
CINTERVAL; ! requesting confidence intervals
TITLE: Three-Step Categorical SEM. See Mplus Web Notes: No.
15, Version 6 (Asparouhov & Muthén, 2013) for method to
calculate the classification uncertainty rates used in the MODEL
command
DATA: FILE IS F:\Data.dat; ! file containing predicted IPV and
SUD classes
VARIABLE:
NAMES ARE Family SUDClass IPVClass;
USEVARIABLES = SUDClass IPVClass;
NOMINAL = SUDClass IPVClass; ! defining indicators as
nominal
MISSING = ALL(-9999); ! defining missing data value
CLASSES = cSUD(3) cIPV(3); ! defining latent class names
and number of classes
ANALYSIS:
TYPE = MIXTURE; ! requesting a mixture analysis
MODEL: ! model specification follows
%OVERALL% ! defining effects are for all classes
cIPV ON cSUD; ! regression IPV class on SUD class
MODEL cSUD: ! defining LCA for SUD variables
%cSUD#1% ! defining below effects are for first SUD class
only
[SUDClass#1 @8.279]; ! fixing class means to account for
classification uncertainty rate
[SUDClass#2 @5.180]; ! fixing class means to account for
classification uncertainty rate
%cSUD#2% ! defining below effects are for second SUD class
only
[SUDClass#1 @0.209]; ! fixing class means to account for
classification uncertainty rate
[SUDClass#2 @3.407]; ! fixing class means to account for
classification uncertainty rate
%cSUD#3% ! defining below effects are for third SUD class
only
[SUDClass#1 @-4.989]; ! fixing class means to account for
classification uncertainty rate
[SUDClass#2 @-2.439]; ! fixing class means to account for
classification uncertainty rate
MODEL cIPV: ! defining LCA for IPV variables
%cIPV#1% ! defining below effects are for first IPV class only
[IPVClass#1 @13.766]; ! fixing class means to account for
classification uncertainty rate
[IPVClass#2 @10.789]; ! fixing class means to account for
classification uncertainty rate
%cIPV#2% ! defining below effects are for second IPV class
only
[IPVClass#1 @-1.242]; ! fixing class means to account for
classification uncertainty rate
[IPVClass#2 @2.697]; ! fixing class means to account for
classification uncertainty rate
%cIPV#3% ! defining below effects are for third IPV class
only
[IPVClass#1 @-13.761]; ! fixing class means to account for
classification uncertainty rate
[IPVClass#2 @-2.881]; ! fixing class means to account for
classification uncertainty rate
OUTPUT:
CINTERVAL; ! requesting confidence intervals
Note: Note. For commands to conduct LCA-C and LCA-D with continuous auxiliary variables in Mplus, see Asparouhov and Muthén (2013).
Submitted: July 30, 2012 Revised: December 3, 2012 Accepted: December 4, 2012
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Source: Psychology of Addictive Behaviors. Vol. 28. (1), Mar, 2014 pp. 257-267)
Accession Number: 2013-20765-001
Digital Object Identifier: 10.1037/a0031487
Record: 36- Nonjudging facet of mindfulness predicts enhanced smoking cessation in Hispanics. Spears, Claire Adams; Houchins, Sean C.; Stewart, Diana W.; Chen, Minxing; Correa-Fernández, Virmarie; Cano, Miguel Ángel; Heppner, Whitney L.; Vidrine, Jennifer I.; Wetter, David W.; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 918-923. Publisher: American Psychological Association; [Journal Article] Abstract: Although most smokers express interest in quitting, actual quit rates are low. Identifying strategies to enhance smoking cessation is critical, particularly among underserved populations, including Hispanics, for whom many of the leading causes of death are related to smoking. Mindfulness (purposeful, nonjudgmental attention to the present moment) has been linked to increased likelihood of cessation. Given that mindfulness is multifaceted, determining which aspects of mindfulness predict cessation could help to inform interventions. This study examined whether facets of mindfulness predict cessation in 199 Spanish-speaking smokers of Mexican heritage (63.3% male, mean age of 39 years, 77.9% with a high school education or less) receiving smoking cessation treatment. Primary outcomes were 7-day abstinence at weeks 3 and 26 postquit (biochemically confirmed and determined using an intent-to-treat approach). Logistic random coefficient regression models were utilized to examine the relationship between mindfulness facets and abstinence over time. Independent variables were subscales of the Five Facet Mindfulness Questionnaire (Observing, Describing, Acting With Awareness, Nonjudging, and Nonreactivity). The Nonjudging subscale (i.e., accepting thoughts and feelings without evaluating them) uniquely predicted better odds of abstinence up to 26 weeks postquit. This is the first known study to examine whether specific facets of mindfulness predict smoking cessation. The ability to experience thoughts, emotions, and withdrawal symptoms without judging them may be critical in the process of quitting smoking. Results indicate potential benefits of mindfulness among smokers of Mexican heritage and suggest that smoking cessation interventions might be enhanced by central focus on the Nonjudging aspect of mindfulness. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Nonjudging Facet of Mindfulness Predicts Enhanced Smoking Cessation in Hispanics / BRIEF REPORT
By: Claire Adams Spears
Department of Psychology, The Catholic University of America;
Sean C. Houchins
Department of Psychology, The Catholic University of America
Diana W. Stewart
Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
Minxing Chen
Department of Biostatistics, The University of Texas MD Anderson Cancer Center
Virmarie Correa-Fernández
Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
Miguel Ángel Cano
Department of Epidemiology, Florida International University
Whitney L. Heppner
Department of Psychology, Georgia College & State University
Jennifer I. Vidrine
Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
David W. Wetter
Department of Psychology, Rice University, and Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
Acknowledgement: This work was supported by the National Center on Minority Health and Health Disparities through Grant P60MD000503 and by the National Cancer Institute through The University of Texas MD Anderson Cancer Center’s Support Grant CA016672 and the Latinos Contra el Cancer Community Networks Program Center Grant U54CA153505. This work was also supported by the National Center for Complementary and Integrative Health under Award Number K23AT008442 and a faculty fellowship from The University of Texas MD Anderson Cancer Center Duncan Family Institute for Cancer Prevention and Risk Assessment, both awarded to Claire Adams Spears. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Given that tobacco use is the leading cause of preventable morbidity and mortality in the United States (Mokdad, Marks, Stroup, & Gerberding, 2004) and that most smokers who attempt to quit are unsuccessful (Centers for Disease Control and Prevention [CDC], 2011), identifying strategies to enhance smoking cessation is critical. This is particularly important among Hispanics, who represent the largest ethnic minority group in the United States (U.S. Census Bureau, 2014) and experience profound health disparities (Myers, 2009). Although the prevalence of smoking is lower among Hispanics than in the general U.S. population (12.5% vs. 18.1%; Agaku, King, & Dube, 2014), three of the four leading causes of death in Hispanics are related to smoking (Kochanek, Xu, Murphy, Miniño, & Kung, 2011). Hispanics living in the United States experience culturally related stressors (including discrimination and acculturative stress) that have harmful consequences for mental health and smoking cessation (Kendzor et al., 2014; Torres, Driscoll, & Voell, 2012). Identifying strategies to enhance cessation despite high levels of stress in this understudied and underserved population is critical. The current study sought to investigate whether specific aspects of mindfulness predict smoking cessation among smokers of Mexican heritage.
Mindfulness has been defined as “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally” (Kabat-Zinn, 1994, p. 4). This form of nonjudgmental attention should foster self-acceptance in the midst of difficult life experiences and lessen the likelihood of impulsive reactions to stress. Indeed, research suggests that mindfulness-based training reduces emotional reactivity in the face of stressors (Arch & Craske, 2010; Britton, Shahar, Szepsenwol, & Jacobs, 2012). Dispositional mindfulness, the tendency for mindful responding in daily life, is associated with lower perceived stress, depressive symptoms, and neuroticism (Brown & Ryan, 2003). There is also growing evidence that mindfulness-based interventions, aimed at increasing dispositional mindfulness, enhance smoking cessation (Brewer et al., 2011; Davis, Fleming, Bonus, & Baker, 2007; Davis, Goldberg, et al., 2014; Davis, Manley, Goldberg, Smith, & Jorenby, 2014). These trials included 10.3%, 0.0%, 1.5%, and 1.7% Hispanics, respectively, highlighting the need for research on mindfulness and smoking cessation in this population.
Vidrine, Businelle et al. (2009) reported that among smokers interested in quitting (10% of whom were Hispanic), those with greater mindfulness indicated lower nicotine dependence, lower withdrawal severity, higher self-efficacy for avoiding smoking in high-risk situations, and greater expectancies that they could control emotions without smoking. Moreover, Heppner et al. (2015) found that among African American smokers receiving cessation treatment, those with greater mindfulness were more likely to be abstinent up to 26 weeks postquit. Thus, initial research suggests that mindfulness is linked to improved cessation outcomes; however, more work is needed to determine whether this association exists among Hispanic populations.
In addition, research is needed to clarify which aspects of mindfulness might promote smoking cessation. Although mindfulness has been conceptualized as multifaceted (Baer, Smith, Hopkins, Krietemeyer, & Toney, 2006), the aforementioned studies of dispositional mindfulness and cessation used unidimensional measures of mindfulness. Baer et al. (2006) conducted an influential factor analysis of mindfulness questionnaires that revealed five facets: (a) Observing (paying attention to present sensations), (b) Describing (labeling thoughts and feelings), (c) Acting With Awareness (staying focused on the present moment and acting deliberately), (d) Nonjudging (accepting thoughts and feelings without judging them), and (e) Nonreactivity (perceiving thoughts and feelings without reacting to them). Although these facets are related, they can be distinguished conceptually. For example, a person may be highly attuned to emotions (i.e., observing anxiety) but not be able to describe them in words or refrain from judging them as negative. The work by Baer et al. resulted in the Five Facet Mindfulness Questionnaire (FFMQ).
Research has begun to examine differential associations between FFMQ facets and psychosocial functioning. Cebolla et al. (2012) found that in an adult Spanish sample, Nonjudging, Acting With Awareness, Describing, and Nonreactivity were each related to lower psychiatric symptoms, but Observing was not. Among the subscales, the Nonjudging facet showed the strongest associations with lower psychiatric symptoms. Although no known research has examined associations between FFMQ subscales and smoking cessation, Roberts and Danoff-Burg (2010) found that Acting With Awareness was associated with smoking fewer cigarettes per day among college students. Additional research suggests that Nonjudging, Describing, and Acting With Awareness are related to lower eating pathology and alcohol use (Adams et al., 2012; Fernandez, Wood, Stein, & Rossi, 2010). The Observing facet may only predict better psychosocial functioning and healthier behaviors among experienced meditators, who have practiced observing sensations with a nonjudgmental, nonreactive stance (Baer et al., 2008). In fact, in nonmeditating samples, greater observation of experiences may be maladaptive if individuals are prone to focusing on unpleasant thoughts and emotions with a judgmental attitude. Thus, we did not expect Observing to predict smoking cessation in the current sample of nonmeditators.
Notably, none of the above studies on mindfulness and health risk behaviors focused on Hispanics living in the United States. The current study is the first known to examine associations between dispositional mindfulness and smoking cessation in a Hispanic population. In a sample of Spanish-speaking smokers of Mexican heritage, we sought to examine whether specific facets of mindfulness predict smoking cessation over time. Hispanics living in the United States frequently experience stress related to social disadvantage, discrimination, and acculturation, and these stressors can impede efforts to quit smoking and contribute to health disparities (Kendzor et al., 2014; Myers, 2009; Torres et al., 2012). Mindfulness appears to promote enhanced emotion regulation in stressful situations (Arch & Craske, 2010; Britton et al., 2012), and Hispanic smokers who are able to notice uncomfortable experiences nonjudgmentally and without automatically reacting to them might be less likely to smoke in an attempt to relieve distress. Thus, we hypothesized that the Nonjudging and Nonreactivity FFMQ subscales (which focus on how participants respond to distressing thoughts, emotions, and situations) would predict abstinence. Determining which specific aspects of mindfulness are linked to cessation could be critical to inform mindfulness-based smoking cessation treatments for Hispanic populations.
MethodData were collected as part of a longitudinal study examining predictors of smoking cessation among Spanish-speaking adults of Mexican heritage. As part of this clinical research study, participants received smoking cessation treatment, including nicotine patch therapy, self-help materials, and six brief in-person and telephone counseling sessions based on an empirically validated intervention for Spanish-speaking smokers (Wetter et al., 2007). All participants received the same treatment, which was based on the Treating Tobacco Use and Dependence clinical practice guideline (Fiore et al., 2000) and motivational interviewing (Miller & Rollnick, 2002) and did not specifically teach mindfulness. Questionnaire data (including trait mindfulness) were collected at baseline (1 week before the quit date), and biochemically confirmed smoking cessation was assessed at 3 and 26 weeks postquit. Procedures were approved by the institutional review board of The University of Texas MD Anderson Cancer Center, and all participants completed the informed consent process.
Participants
In total, 199 participants were recruited through media advertising (n = 165) in the Houston area or through the population-based Mexican American Cohort Study (n = 34), a longitudinal study of health risk factors among individuals of Mexican heritage (Barcenas et al., 2007). Individuals were eligible if they (a) were of Mexican heritage, (b) preferred to speak Spanish, (c) were 18–65 years old, (d) were a current smoker having smoked ≥5 cigarettes/day in the past year, (e) had an expired carbon monoxide (CO) level of ≥8 ppm (Benowitz et al., 2002), (f) were motivated to quit smoking in the next month, and (g) possessed a valid home address and home telephone number. Exclusion criteria were (a) contraindication for use of the nicotine patch, (b) active substance use disorder, (c) regular use of tobacco products other than cigarettes, (d) use of bupropion or nicotine replacement products other than patches supplied by the study, (e) pregnancy or lactation, (f) another household member enrolled in the study, or (g) participation in another smoking cessation program or research study within the past 90 days.
Measures
Demographic information
Participants indicated their age, gender, partner status (married/living with partner vs. single/divorced/separated/widowed), and educational attainment.
Nicotine dependence
The Heaviness of Smoking Index (HSI; Heatherton, Kozlowski, Frecker, Rickert, & Robinson, 1989) is a two-item self-report measure that is supported as a reliable and valid indicator of nicotine dependence (Borland, Yong, O’Connor, Hyland, & Thompson, 2010) and has been used in Hispanic smokers (Vidrine, Vidrine, et al., 2009). The two items (administered at baseline) are the following: “How many cigarettes a day do you smoke on average?” and “How soon after you wake up do you smoke your first cigarette?” (“time to first cigarette”).
Mindfulness
The FFMQ (Baer et al., 2006) is a 39-item self-report measure of dispositional mindfulness. Participants rate how often each item describes them from 1 (never or rarely true) to 5 (very often or always true). The FFMQ yields five subscales: (a) Observing (e.g., “I pay attention to sensations, such as the wind in my hair or the sun on my face”), (b) Describing (e.g., “I’m good at finding the words to describe my feelings”), (c) Acting With Awareness (e.g., “I rush through activities without really being attentive to them [reverse-scored]), (d) Nonjudging (e.g., “I think some of my emotions are bad or inappropriate and I shouldn’t feel them” [reverse-coded]), and (e) Nonreactivity (e.g., “I perceive my feelings and emotions without having to react to them”). For the current study, the FFMQ was translated into Spanish using a back-translation procedure by two bilingual individuals of Hispanic origin and reviewed by personnel of the institution’s International Department of Medical Translation. The translated version was then reviewed by Mexican American individuals reflecting diverse levels of acculturation so that consensus on wording was reached. The resulting Spanish FFMQ was administered at baseline. In the current sample, all subscales showed adequate internal consistency (α = 0.71–0.83).
Smoking abstinence
Seven-day point prevalence abstinence at 3 and 26 weeks postquit was defined as self-reported complete abstinence from smoking for the previous 7 days, verified by either CO <8 ppm or salivary cotinine <20 ng/ml. At each time point, participants who reported a lapse and/or produced CO or cotinine levels inconsistent with abstinence were considered not abstinent. An intent-to-treat (ITT) approach was used, such that when abstinence status could not be determined due to missing data, participants were considered not abstinent.
Depressive symptoms
Given that mindfulness is associated with lower depressive symptoms (Brown & Ryan, 2003) and that depressive symptoms often predict worse cessation outcomes (Leventhal, Ramsey, Brown, LaChance, & Kahler, 2008), ancillary analyses controlled for depression. The Center of Epidemiological Studies Depression Scale (CES-D; Radloff, 1977), a psychometrically sound 20-item measure of past-week depressive symptoms, was administered at baseline.
Statistical Analyses
To examine the relationship between baseline mindfulness and abstinence over time, we utilized logistic random coefficients regression models. Models specified an unstructured covariance matrix for the vector of random intercept and slope of time for each participant. Primary outcomes were biochemically confirmed 7-day abstinence at weeks 3 and 26 postquit. First, models were created to predict repeated-measures abstinence from all FFMQ subscales entered simultaneously. Next, separate models were created to predict abstinence from each FFMQ facet. Analyses were conducted with and without controlling for demographic variables (gender, education, age, and partner status, chosen based on past research; e.g., Businelle et al., 2010) and nicotine dependence (HSI). Finally, models were created to examine whether any associations remained significant after controlling for baseline depressive symptoms.
ResultsOf 199 participants, 63.3% were male, 69.3% were married or living with a partner, and 77.9% reported less than or equal to high school education. Mean age was 38.73 years (SD = 10.14). Mean scores on FFMQ subscales were as follows: Observing, M = 24.19 (SD = 6.37); Describing, M = 27.24 (SD = 5.17); Acting With Awareness, M = 30.42 (SD = 5.99); Nonjudging, M = 27.30 (SD = 6.31); and Nonreactivity, M = 19.16 (SD = 4.97).
The Nonjudging facet predicted greater odds of abstinence, both with (OR = 1.06, p = .02) and without (OR = 1.08, p = .01) controlling for demographic covariates and dependence. None of the other subscales were significant predictors in separate models (ps > .15). When subscales were entered simultaneously (rather than in separate models), the same pattern emerged: Nonjudging predicted greater odds of abstinence (OR = 1.09, p = .03), over and above other facets of mindfulness. None of the other facets emerged as significant predictors (ps > .30). After controlling for demographic covariates and dependence, Nonjudging remained a significant predictor of greater odds of abstinence (OR = 1.09, p = .03). Analyses were also conducted using completers only (rather than ITT), and the pattern of results was identical. Finally, given that Nonjudging was associated with lower depressive symptoms, r = −.39, p < .001, baseline CES-D score was entered as a covariate. In separate models, Nonjudging remained a significant predictor after controlling for depression and demographics (OR = 1.06, p = .04); this association approached significance when also controlling for dependence (OR = 1.06, p = .057). In simultaneous models, Nonjudging remained significant after controlling for depression and demographics (OR = 1.09, p = .048) and approached significance after also controlling for dependence (OR = 1.07, p = .07). Although Nonjudging was not significant at the .05 level once all covariates were included, it was still a significant predictor when both depression and demographics were controlled. Thus, the relationship between Nonjudging and abstinence does not appear to be fully explained by lower concurrent depressive symptoms.
In effort to enhance our understanding of the clinical significance of the findings, abstinence rates were examined for participants low versus high on Nonjudging. At week 3 postquit, 22.7% of participants in the lowest quartile of Nonjudging were abstinent, versus 53.8% of those in the highest quartile. At week 26 postquit, only 4.5% of those low in Nonjudging were abstinent, compared to 23.1% of those high in Nonjudging.
DiscussionNonjudgment may be a key aspect of mindfulness, contributing to enhanced cessation outcomes among Spanish-speaking smokers of Mexican heritage. Notably, Bishop et al. (2004) highlighted a nonjudgmental, accepting orientation to experience as one of two core aspects of mindfulness. Learning to experience unpleasant thoughts, feelings, and physical sensations associated with smoking cessation without judging them may lessen distress and increase likelihood of abstinence. Example FFMQ Nonjudging items (reverse-coded) are the following: “I tell myself I shouldn’t be feeling the way I’m feeling” and “When I have distressing thoughts or images, I judge myself as good or bad, depending what the thought/image is about” (Baer et al., 2006). If a person tells himself that he should not be feeling irritable and judges himself as a “bad” person because he is having strong cravings, these judgmental self-statements may further escalate negative emotions, increasing the likelihood of smoking in an attempt to relieve distress (Marlatt & Witkiewitz, 2005). Alternatively, if a person recognizes that unpleasant sensations are a natural part of the quit process (i.e., accepting instead of judging them as “bad” sensations that need to be escaped), he might be more likely to abstain from smoking in the context of these sensations.
Accepting thoughts, feelings, and physical sensations without judgment may be particularly helpful for Hispanic smokers in the United States, for whom discrimination and stress associated with acculturation can increase psychological distress and interfere with cessation (Kendzor et al., 2014; Torres et al., 2012). If individuals are able to notice uncomfortable experiences without judgment, they may be less likely to smoke in an attempt to escape distress. Importantly, mindfulness does not involve ignoring/suppressing or denying thoughts or emotions related to difficult situations. Rather, mindfulness skills encourage individuals to notice distressing experiences (including any associated thoughts and emotions) and then bring their attention back to other features of the present moment so that their responses are flexible, adaptive, and nonimpulsive. It is unclear why the Nonreactivity facet did not predict cessation in this sample; research should continue to examine the relevance of this facet for cessation.
Although no other known research has examined facets of mindfulness with regard to smoking cessation, at least three studies support the unique importance of Nonjudgment in relation to alcohol use. Ostafin, Kassman, and Wessel (2013) found that Nonjudging moderated the association between automatic responses to alcohol and alcohol preoccupation (i.e., Nonjudging weakened the link between automatic emotional reactions to alcohol and difficulty disengaging from alcohol-related thoughts). Ostafin and Marlatt (2008) reported that “Accepting Without Judgment” weakened the link between automatic motivation to drink and problematic alcohol use. Fernandez et al. (2010) found that Nonjudging was uniquely associated with lower alcohol-related consequences. Given that smoking and problematic drinking are often fueled by self-criticism and negative emotions, mindfully experiencing uncomfortable thoughts and feelings without judging them may reduce the likelihood that they will trigger unhealthy behaviors.
The current study is limited by an exclusive focus on Spanish-speaking smokers of Mexican heritage who were motivated to quit smoking, and results may not generalize to smokers who would be ineligible for this study (e.g., smokers who are not motivated to quit, have a substance use disorder, are pregnant, or for whom nicotine patches are contraindicated). The majority (63%) of participants were male, and the results could be more applicable to men than women. In addition, mindfulness was only measured at baseline, and future research should examine how changes in mindfulness over time relate to abstinence outcomes. This study is strengthened by its focus on an underserved ethnic group, examination of multiple facets of mindfulness, use of longitudinal data, and biochemical confirmation of smoking status.
Results highlight the importance of the Nonjudging facet of mindfulness in predicting enhanced smoking cessation outcomes in smokers of Mexican heritage. Notably, this study examined dispositional mindfulness (i.e., naturally occurring individual differences) rather than mindfulness-based treatment. Research should examine whether mindfulness-based treatment enhances certain aspects of mindfulness and whether increases in mindfulness facets lead to higher abstinence rates. Smoking cessation interventions that encourage mindful experience of thoughts, emotions, and physical sensations without judgment might be effective for enhancing smoking cessation among Hispanics and potentially for other populations of smokers as well.
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Submitted: November 14, 2014 Revised: March 19, 2015 Accepted: March 19, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 918-923)
Accession Number: 2015-20853-001
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Record: 37- Parallel demand–withdraw processes in family therapy for adolescent drug abuse. Rynes, Kristina N.; Rohrbaugh, Michael J.; Lebensohn-Chialvo, Florencia; Shoham, Varda; Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014 pp. 420-430. Publisher: American Psychological Association; [Journal Article] Abstract: Isomorphism, or parallel process, occurs in family therapy when patterns of therapist–client interaction replicate problematic interaction patterns within the family. This study investigated parallel demand–withdraw processes in brief strategic family therapy (BSFT) for adolescent drug abuse, hypothesizing that therapist-demand/adolescent-withdraw interaction (TD/AW) cycles observed early in treatment would predict poor adolescent outcomes at follow-up for families who exhibited entrenched parent-demand/adolescent-withdraw interaction (PD/AW) before treatment began. Participants were 91 families who received at least four sessions of BSFT in a multisite clinical trial on adolescent drug abuse (Robbins et al., 2011). Prior to receiving therapy, families completed videotaped family interaction tasks from which trained observers coded PD/AW. Another team of raters coded TD/AW during two early BSFT sessions. The main dependent variable was the number of drug-use days that adolescents reported in timeline follow-back interviews 7 to 12 months after family therapy began. Zero-inflated Poisson regression analyses supported the main hypothesis, showing that PD/AW and TD/AW interacted to predict adolescent drug use at follow-up. For adolescents in high PD/AW families, higher levels of TD/AW predicted significant increases in drug use at follow-up, whereas for low PD/AW families, TD/AW and follow-up drug use were unrelated. Results suggest that attending to parallel demand–withdraw processes in parent–adolescent and therapist–adolescent dyads may be useful in family therapy for substance-using adolescents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Parallel Demand–Withdraw Processes in Family Therapy for Adolescent Drug Abuse
By: Kristina N. Rynes
Center on Alcoholism, Substance Abuse, and Addictions (CASAA), University of New Mexico;
Michael J. Rohrbaugh
Department of Psychology, University of Arizona
Florencia Lebensohn-Chialvo
Department of Psychology, University of Arizona
Varda Shoham
Department of Psychology, University of Arizona and the National Institute of Mental Health, Bethesda, Maryland
Acknowledgement: This project was supported by National Institute on Drug Abuse Grants R01-DA17539-01, U10-DA15815, and U10-DA13720 and National Institute on Alcohol Abuse and Alcoholism Grant T32-AA0018108-01A1. We extend appreciation to Brant Hasler, Katherine Calkins, Carlos Figueroa, Katie Gallardo, and Brittany Martell, whose work made the successful completion of this project possible. We also thank David A. Sbarra, Emily A. Butler, and Barbara S. McCrady for their helpful comments on this article.
The efficacy of family therapy for adolescent substance abuse is well documented. Many studies have demonstrated that, compared with individual therapy and treatment as usual, generic family therapy is associated with greater reductions in adolescent substance use and other types of positive behavior change (cf. Rowe, 2012; Stanton & Shadish, 1997; Waldron & Turner, 2008 for reviews). Specific models, such as multidimensional family therapy (Liddle, 2002) and functional family therapy (Sexton & Alexander, 2002), have achieved the status of “well-established treatments” in this arena (cf. Waldron & Turner, 2008). Brief strategic family therapy (BSFT)—a relatively pure form of systemic therapy, and the focus of this report—has demonstrated clear promise as well (Robbins et al., 2011; Santisteban et al., 2003; Szapocznik et al., 1989; Williams & Chang, 2000).
As outcome evidence accumulates, researchers are recognizing that process studies are needed to determine how and for whom family interventions work (Rowe, 2012). By understanding the mediators and moderators of family therapy, researchers will be able to refine family interventions by emphasizing the aspects that are most highly associated with positive outcomes and eliminating the aspects that are inert. Such refinement efforts will help make family therapy models more efficient and cost-effective (Kazdin & Nock, 2003; La Greca, Silverman, & Lochman, 2009).
In the current study, we investigate a process hypothesis based on the systemic notion of isomorphism, or parallel process—namely, that poor outcomes are likely when patterns of therapist–client interaction replicate, or resemble in form, problematic patterns of interaction within the family itself. In the context of family therapy, isomorphism occurs when relational roles or patterns in one part of the therapy system correspond closely to the roles or interaction patterns in another subsystem (Liddle & Saba, 1983, 1985; Schwartz, Liddle, & Breunlin, 1988). For example, Liddle and Saba (1983) described the supervisory system and the therapy system as isomorphic: the supervisor’s role as “teacher” to the therapist is similar to the therapist’s role as an agent of learning and change to the family. Liddle and Saba (1983) also described isomorphism that develops within the therapy system, where therapists form relationships with family members that are isomorphic to existing relationships within the family. For instance, if a therapist tries to change the behavior of an adolescent in the same way that a parent tries to change the adolescent, the therapist–adolescent relationship can become isomorphic to the parent–adolescent relationship.
The current study focuses on a communication pattern called demand–withdraw interaction (DW) and investigates whether isomorphic DW processes in the therapist-adolescent and parent-adolescent subsystems of BSFT predict adolescent drug use outcomes. DW interaction is a dyadic pattern of communication in which one family member nags, criticizes, or pressures another family member to change, while the other ignores, distances, or refuses to discuss the topic (Eldridge & Christensen, 2002; Shoham, Rohrbaugh, & Cleary, 2008). Researchers have focused mainly on DW in couples, where this pattern correlates strongly with relationship dissatisfaction (Christensen, 1987; Christensen & Heavey, 1990; Heavey, Layne, & Christensen, 1993; Noller & White, 1990), domestic violence (Babcock, Waltz, Jacobson, & Gottman, 1993; Holtzworth-Munroe, Smutzler, & Stuart, 1998), and alcohol abuse (Kelly, Halford, & Young, 2002). Two studies have also investigated DW in parent–child dyads, finding that it correlated with relationship dissatisfaction of both parents and adolescent children as well as increased adolescent substance use (Caughlin & Malis, 2004a, 2004b).
From a family-systems perspective, DW is a problem-maintaining process that is fueled by both the demanding partner’s wish to change the other and the withdrawn partner’s desire to ignore the demanding partner in an attempt to preserve the status quo (Rohrbaugh & Shoham, 2001; Watzlawick, Bavelas, & Jackson, 1967). An example of a DW interaction that might occur in the family of a drug-abusing adolescent could involve a mother pressuring her son to quit using drugs while the son withdraws from her. The more the mother demands, the more the son ignores her; and the less likely it becomes that he will follow her advice—an ironic outcome given the mother’s wish for her son to stop using drugs.
The observation that DW often reflects one family member’s attempts to change another receives support from studies of couples, showing that demand and withdraw roles change according to the importance of the discussion topic for each partner (Heavey et al., 1993; Klinetob & Smith, 1996). For example, Klinetob and Smith (1996) found that when a discussion topic centered on something the wife wanted to change about her husband, wife-demand/husband-withdraw was more likely to occur than husband-demand/wife-withdraw. If the couple was discussing something the husband wanted to change about his wife, however, a husband-demand/wife-withdraw pattern was more likely to occur.
In view of this, social contexts like couple or family therapy, where people at different levels of an interactional system often attempt to induce change in another person’s behavior, provide fertile ground for parallel processes to develop. Thus, in family therapy for adolescent drug abuse, a therapist’s ultimate goal may parallel the goal of a parent—namely, to change the drug-use behavior of the adolescent identified patient (IP). If a central dynamic in the family is a parent-demand/adolescent-withdraw (PD/AW) dynamic, a therapist who replicates the parental stance by putting direct pressure on the adolescent IP to change risks provoking the IP to withdraw from the therapist in the same way he or she withdraws from the demanding parent(s). If the pattern repeats, and a therapist-demand/adolescent-withdraw (TD/AW) cycle develops, it is not difficult to imagine how such an escalating pattern could undermine successful family therapy and contribute to poor IP drug-use outcomes.
Empirical support for these ideas about parallel DW processes in couple and family therapy comes from a study comparing cognitive–behavioral therapy (CBT) and family-systems therapy (FST) for couples in which the male partner was alcoholic (Shoham, Rohrbaugh, Stickle, & Jacob, 1998). The CBT approach placed a high demand on the drinker to abstain from alcohol use, whereas FST therapists remained neutral about abstinence until the couple decided they were ready to change. Prior to therapy, all couples participated in a video-recorded interaction task in which they discussed a conflict as well as the husband’s drinking. From these recordings, investigators later derived observational measures of DW interaction. Strikingly, couples high on wife-demand/husband-withdraw were much more likely than couples low on this pattern to drop out of the high-demand CBT therapy, whereas DW had little bearing on continuance in low-demand FST. The authors interpreted this result to suggest that drinkers responded to a demanding therapy (or therapist) in ways that paralleled their response to a demanding partner or spouse.
The current study investigates parallel DW processes in family therapy for adolescent drug abuse, hypothesizing that TD/AW interaction cycles observed early in treatment would predict poor adolescent outcomes at follow-up for families who exhibited entrenched PD/AW interaction before treatment began. The main participant and outcome data are from a multisite effectiveness study of BSFT for adolescent drug abuse (Robbins et al., 2011; Szapocznik, Hervis, & Schwartz, 2003) administered from the University of Miami as protocol CTN-0014 in the National Institute on Drug Abuse (NIDA) Clinical Trials Network. The observational ratings of parent–adolescent and therapist–family interaction, on the other hand, come from a NIDA platform study conducted at the University of Arizona.
Because BSFT is a “structural” variant of family therapy, related to the work of Minuchin, Haley, and others (Haley, 1976; Madanes, 1981; Minuchin, 1974; Minuchin & Fishman, 1981; Nichols, 2010), the place of DW in this framework deserves comment. For one thing, BSFT focuses less on DW and other dyadic interaction patterns than it does on structural patterns like enmeshment, disengagement, cross-generation coalitions, and hierarchical anomalies in the broader family system (cf. Szapocznik et al., 2003). However, despite its central concern with relationship structure, the actual practice of BSFT focuses largely on interrupting specific patterns of interaction (behavioral sequences) that define this structure. For example, when working with a family in which some members are emotionally disengaged from one another, a BSFT therapist would want to change the interaction patterns that maintain this disengagement—and patterns of DW are likely to figure prominently in this. Another consideration is that good BSFT therapists are active, direct, and even to some extent demanding, as their prescribed role is to orchestrate change in the family system by actively restructuring relational patterns associated with the adolescent’s drug abuse (Szapocznik et al., 2003). Equally and perhaps more important, however, is for the therapist to remain decentralized and work through the family hierarchy to help parents more effectively nurture and control their children. This implies directing interventions (including therapeutic demands for change) toward parental figures more than children. In other words, a central goal of BSFT is to reorganize the family so that the parent figures are in a leadership position, which in practice involves placing more responsibility for change on parents than on children. Thus, therapist demand on adolescents and TD/AW interaction is not consistent with the BSFT model.
This study tested two hypotheses. First, consistent with previous research (Caughlin & Malis, 2004b), we expected that PD/AW would be associated with greater IP drug use at both baseline and follow-up. Second, we expected that TD/AW would moderate the association between PD/AW and IP drug use at follow-up, such that TD/AW would predict increased drug use for IPs with high baseline levels of PD/AW but not for those with low baseline PD/AW.
Method Participants
Participating adolescent IPs and families met two sets of inclusion criteria—one for the parent study and another for the more fine-grained observational analyses reported here. The parent study recruited 13- to 17-year-old clients from eight community treatment programs (CTPs), including one site each in Arizona, California, Colorado, North Carolina, Ohio, and Puerto Rico, and two sites in Florida. Adolescents were included if they reported using illicit drugs other than alcohol or tobacco in the 30-day period preceding their baseline assessment or had been referred from an institution (e.g., detention or residential treatment) for the treatment of a substance-use disorder. They were excluded if they did not reside in the same home as a parent figure, if they reported suicidal or homicidal ideation, or if they had current or pending severe criminal charges.
A narrower set of criteria was necessary to ensure sufficiently complete data for examining parallel DW processes. Families needed to have participated in at least four therapy sessions for which there were at least two adequate (ratable) video recordings. Treatment as usual (TAU) was not videotaped; thus, only BSFT cases were included. Families also needed to have completed a baseline family interaction task (FIAT) with good enough video and audio quality for the observational ratings and to have completed timeline follow-back (TLFB) interviews assessing IP substance use through at least 8 months of the 12-month follow-up period. Of the 245 cases randomized to BSFT, 91 met these criteria.
Table 1 shows demographic characteristics of adolescents and families in the study sample, and Table 2 contains information regarding therapists’ characteristics. As Table 1 shows, about 80% of the adolescents were male and 34% were white. Almost half of the adolescents had a previous history of incarceration. Chi-square analyses and t-tests showed no differences with regard to sex, ethnicity, blended family status, household income, family size, or history of incarceration (all ps ≥ .05) between the 91 IPs included in the current study sample and the other 389 parent-study participants who were not included. Adolescents in the study sample did, however, use drugs on more days in the month prior to baseline than did adolescents not included in the study, t(473) = −2.27, p = .02. For therapist participants, there were no differences in age, sex, ethnicity, degree earned, or years of counseling experience between the 18 included therapists and the 31 who were not included, all ps ≥ .12.
Adolescent and Family Demographic Characteristics
Therapist Demographic Characteristics
Procedure
In the BSFT clinical trial, investigators at the University of Miami recruited adolescents and families from participating CTPs, randomized them to treatment condition, and collected self-reported drug-use data from adolescents. The Miami team also developed a BSFT manual (Szapocznik et al., 2003), recruited therapists from participating CTPs, randomized them to treatment condition, trained therapists in how to implement BSFT, and continuously monitored the progress of the BSFT therapists via supervision and adherence ratings of videotaped therapy sessions (cf. Robbins et al., 2011 for details about these procedures). Investigators at the University of Arizona coordinated the administration of videotaped structured FIATs at baseline, from which a team of research assistants (RAs) later rated PD/AW. A second, independent group of Arizona RAs rated videos of selected BSFT sessions to code the therapist’s level of demand for IP change and the IP’s response to these demands (generating the measure of TD/AW). The institutional review boards of the University of Miami, the University of Arizona, and each participating research site approved the study procedures.
Descriptive statistics indicated that BSFT families in the clinical trial received an average of 8.1 (SD = 5.2) therapy sessions over 5.7 (SD = 3.2) months. In contrast, families in the present subsample received an average of 11.3 (SD = 4.5) sessions over 6.6 (SD = 2.7) months. Most participants (58.2%) had completed BSFT by the 6-month assessment. Therapy sessions lasted about 1 hour and took place in families’ homes (63%) or in clinic settings (32%).
Observational measures of PD/AW came from video-recorded FIATs administered by RAs at each site prior to the initiation of treatment. RAs asked that all family household members over the age of 6 gather in a place that was comfortable and convenient for them, such as their home or the CTP facility. The RAs then administered three sequential FIAT tasks based on earlier work by Minuchin, Montalvo, Guerney, Rosman, and Schumer (1967) and the Miami group (e.g., Santisteban et al., 2003). The tasks were (a) plan a dinner menu, (b) discuss likes and dislikes about each family member, and (c) discuss a recent family argument. To ensure that all families received the same task information, RAs used audiotaped instructions to initiate each task. On average, FIATs included 3.7 (SD = 1.4) family members and in 48.4% of FIATs, two parental figures were present. It took families an average of 2.4 (SD = 1.2) minutes to complete the first FIAT task, 4.9 (SD = 3.6) minutes to complete the second task, and 4.2 (SD = 2.7) minutes to complete the third task. Total FIAT time averaged 11.5 minutes (SD = 6.3).
To capture TD/AW, the first author and a team of four trained RAs coded levels of therapist demand and the adolescent IP’s response to that demand. The team rated three 5-min segments of two BSFT sessions, the first occurring early in the process of therapy (Sessions 1–4) and the second occurring about midway through therapy (Sessions 5–7). As a rule, we selected the first usable (ratable) session in each of these blocks, and the rationale for sampling from two blocks was to estimate the consistency of therapist-demand behavior across sessions and to include a session likely to involve active intervention (e.g., restructuring). Within each session, the team observed and rated the first 5 min, another 5 min occurring 40% of the way into the session, and a final 5 min occurring 80% of the way into the session.
Measures
Adolescent drug use
Each month for 12 months, parent-study RAs blind to treatment condition administered an adolescent-specific version of the TLFB interview (Bry, Conboy, & Bisgay, 1986; Bry & Krinsley, 1992; Sobell & Sobell, 1992) to all adolescent IPs. The RAs also conducted monthly urine drug screens using the SureStep Drug Screen Card 10A (Orlando, FL) to encourage accurate reporting of drug use.
The main outcome variable in this process study was the number of days that adolescent IPs reported using drugs in the 7–12 month follow-up period. The number of IP drug-use days in the month prior to baseline was included as a covariate in each study analysis. Past research has shown that responses on the TLFB interview are reliable, yielding consistently high test–retest correlations (Mason, Cauce, Gonzales, Hiraga, & Grove, 1994) and month-to-month stability coefficients in the present study were high as well (all rs ≥ .78, all ps ≤ .0001).
Parent-Demand/Adolescent-Withdraw (PD/AW)
Observational ratings of FIATs assessed the amount of PD/AW in each family at baseline. At least 2 raters independently coded all FIATs using Global Structural Family Systems Ratings (GSFSR), a coding system that includes scales for rating both family structure and specific family-interaction patterns, including PD/AW (Rohrbaugh, Hasler, Lebensohn-Chialvo, & Shoham, 2007). The GSFSR defines PD/AW as a pattern in which the parent(s) request, demand, nag, blame, criticize or try to discuss a problem with the IP while the IP becomes silent or disengaged, refuses to discuss the issue, or diverts attention away from the issue. Raters assessed the level of PD/AW in each task of the baseline FIATs using two rating scales, one measuring mother-demand/adolescent-withdraw (MD/AW) and another measuring father-demand/adolescent-withdraw (FD/AW). A score of 1 indicated no evidence of DW occurring at any time during the task, and a score of 5 indicated pervasive evidence of DW occurring throughout most of the task. Interrater reliability was continually monitored while ratings were taking place, and ICCs were consistently greater than .60. When ratings differed by more than 1.5 scale points, coders rereviewed the FIAT to arrive at a consensus rating.
We performed a series of repeated-measures ANOVAs to understand specific patterns of PD/AW across tasks and roles. There was a main effect of task, F(1, 90) = 30.09, p < .001 with the highest PD/AW scores occurring in Task 3 (family argument) and each successive task generating significantly higher scores than the previous task, ps < .01. Somewhat surprisingly, given the emphasis on gender roles in the DW literature, there was no significant main effect of parental role (MD/AW vs. FD/AW) and no statistical interaction between parental role and task (ps > .80), suggesting that the balance of MD/AW and FD/AW was similar in each task. Nor did the average amount of PD/AW in families that had two parent figures present for the FIAT (M = 1.31, SD = .37) differ from the average amount of PD/AW in families with only one parent present (M = 1.49, SD = .70), F(1, 89) = 2.30, p = .13.
In tests of the study hypotheses, the measure of PD/AW at baseline reflects the highest level of PD/AW recorded in interactions between the adolescent and either parent in any of the three FIAT tasks. In other words, the family-level score could represent either MD/AW or FD/AW (depending which was greater) and could come from discussions of menus (Task 1), likes and dislikes (Task 2), or family arguments (Task 3). We chose to measure PD/AW using this maximum score to ensure that we captured PD/AW whenever it occurred. In some FIAT tasks, the maximum rating of PD/AW was “1,” meaning that PD/AW never occurred. Specifically, in Task 1, 86% of families never engaged in PD/AW, in Task 2, 58.2% of families never engaged in PD/AW, and in Task 3, 48.4% did not engage in PD/AW. Using the highest rating of PD/AW from each family’s baseline FIAT maximized the probability that our measure captured instances of PD/AW. Nevertheless, families’ scores on this maximum PD/AW variable were relatively low, with a mean of 1.9 (SD = 1.0) on the 1–5 rating scale.
Parental monitoring
To assess the differential construct validity of the PD/AW ratings, we compared them to self-reports of parental monitoring, a set of parenting practices that have been shown to predict decreased adolescent drug use and behavior problems (cf. Dishion, Li, Spracklen, Brown, & Haas, 1998; Patterson & Stouthamer-Loeber, 1984). The parent study assessed parental monitoring at baseline using the parental monitoring subscale from the Parenting Practices Questionnaire (Thornberry, Huizinga, & Loeber, 1995). This subscale consists of 13 items on the adolescent version and 12 items on the parent version of the Parenting Practices Questionnaire. Representative items include: “When was the last time that you discussed with your child his or her plans for the coming day?” and “When your child is out, do you know what time he or she will be home?” Participants rated these items on 1–5-point scales where 1 indicated don’t know and 5 indicated almost every day. Parent study investigators found that the parental monitoring subscale had good internal consistency (Cronbach’s αs ≥ .72) for both adolescents and parents (Feaster et al., 2010).
Therapist-Demand/Adolescent-Withdraw (TD/AW)
The research team developed original observational coding scales to rate therapist demand on the adolescent IP and the IP’s response to this demand. We conceptualized therapist demand as involving requests that the adolescent change some behavior, or accept some viewpoint or definition of a behavioral reality (e.g., that drug use is “dangerous,” talking back “disrespectful,” and so on). Two 5-point scales captured, respectively, the extent and negative valence of therapist demand. On the extent scale, a rating of 1 indicated that the therapist never requested the IP to change either his or her behavior or perceptions of something, and 5 indicated that the therapist either did this very frequently or made more than one particularly salient demand for change. On the negative valence scale, raters coded the degree to which the therapist’s demands were critical, hostile, judgmental, or accusatory, with 1 indicating no negative valence and 5 very high negative valence (with one or two very salient examples justifying a score of 5). Interrater reliability for both of these observational scales was very good, with all ICCs ≥ .81.
On the adolescent IP side, two 5-point scales measured IP response to therapist demands. First, an accept–reject scale indicated whether IPs accepted or refused to comply with the therapist’s demands. Here a rating of 1 indicated consistent acceptance of the therapist’s demands and 5 indicated consistent refusal. Second, an active rejection response scale captured how passively or actively the IP rejected the therapist’s demands by attempting to change the subject, becoming defensive, or justifying him or herself. Here a rating of 1 indicated that all IP rejections were passive and 5 indicated that all were active. Scores on these IP-response scales had good interrater reliability, ICCs ≥ .72.
The measure of TD/AW interaction consisted of an aggregation of the ratings of therapist demand and IP response across the two rated sessions. Ratings of the extent and negative valence of therapist demand were positively correlated (r = .42, p < .0001). Thus, we averaged these ratings to form an aggregate measure of therapist demand. Ratings on the accept–reject and active rejection scales measuring IP withdrawal were also positively correlated (r = .33, p = .001). Thus, we averaged these scores to form an aggregate measure of IP withdrawal. The aggregate therapist-demand measure from the early session correlated with the aggregate therapist-demand score from the middle session (r = .54, p < .0001) and the aggregate IP-withdrawal score from the early session correlated with the aggregate IP-withdrawal score from the middle session, r = .33, p < .002, indicating that therapist demand and IP withdrawal were consistent over time. Therefore, we averaged therapist-demand ratings across sessions and the IP withdrawal across sessions to create final measures of each of these constructs. A final analysis showed that the average amount of therapist demand across sessions significantly correlated with the average amount of IP withdrawal across sessions, r = .47, p < .0001. Thus, we created a final measure of TD/AW by adding these two aggregate scores and dividing the sum by 2.
Analytic Strategy
To test the study hypotheses, we used zero-inflated Poisson (ZIP) regression analyses and executed these analyses using the “pscl” package (Zeileis, Kleiber, & Jackman, 2008) in R (R Development Core Team, 2009). ZIP regression is appropriate for analyzing nonnormally distributed dependent variables that consist of a number of discrete events (e.g., drug-use days) when the most common frequency count is zero and the frequency of the remaining counts have a Poisson distribution. The distribution of drug-use days in treatment studies is often nonnormal and well characterized by such ZIP distributions (cf. Hildebrandt, McCrady, Epstein, Cook, & Jensen, 2010). This was true in the present study: 13.5% of adolescents never used drugs during follow-up and the frequencies of the remaining counts of drug-use days were much lower (≤5.5%) and followed a Poisson distribution.
ZIP models contain two components, namely a binomial logistic regression that estimates the odds of being in the zero class (e.g., the odds of achieving complete abstinence from drugs) and a Poisson regression that estimates the Poisson mean of all values of the dependent variable, e.g., the number of drug-use days (Zeileis et al., 2008). Thus, ZIP analyses provide two sets of parameter estimates, one that indicates the extent to which each independent variable predicts the probability of achieving complete abstinence and another that indicates the extent to which each independent variable is associated with the number of drug-use days in the follow-up period.
Results Descriptive Statistics and Preliminary Analyses
Table 3 presents statistics describing the variables included in the study’s main analyses, namely, adolescents’ drug use, PD/AW interaction at baseline, and TD/AW interaction. This table shows that the percentage of IPs who were abstinent from drugs significantly decreased from baseline (30%) to follow-up (15%), χ2(1, N = 91) = 5.98, p = .01. This decrease appeared unrelated to whether IPs were recruited from restricted environments at baseline. Indeed, a logistic regression that controlled for baseline abstinence showed that the number of days in the month prior to baseline that IPs spent in restricted environments was not associated with abstinence at follow-up (OR = .08, SE = 2.43, p = .29). There were no significant correlations between PD/AW, TD/AW, baseline drug-use days, and therapists’ years of counseling experience (all ps ≥ .11). Thus, drug-use days at baseline were not related to either TD/AW (r = −.03, p = .79) or PD/AW (r = −.09, p = .40). In addition, PD/AW was not associated with having a history of previous incarceration, t(88) = .88, p = .38 or multiple arrests, t(88) = .05, p = .96. TD/AW, however, was more pronounced for adolescents with a previous history of multiple arrests, t(88) = −2.51, p = .01 but did not differ according to whether adolescents had a history of incarceration, t(88) = .20, p = .84. PD/AW was unassociated with both adolescents’ and parents’ reports of parental monitoring, r = −.06, p = .57 and r = .00, p = 1.00, respectively.
Descriptive Statistics: Adolescent, Family and Therapist Variables
Associations Between PD/AW and IP Drug Use at Baseline and Follow-Up
A ZIP analysis that regressed baseline IP drug-use days on baseline PD/AW, controlling for days that IPs were in restricted environments at baseline, showed that PD/AW was not related to baseline drug-use days (B = −.06, SE = .04, p = .16), but was marginally associated with a greater likelihood of abstinence at baseline, B = .46, SE = .25, p = .06. Number of days spent in a restricted environment before baseline was not related to baseline drug-use days (B = −.03, SE = .02, p = .11) but was associated with a greater likelihood of abstinence at baseline, B = .21, SE = .09, p = .01. A second ZIP analysis that regressed follow-up drug-use days on baseline PD/AW, controlling for baseline drug-use days and days spent in restricted environments at baseline and follow-up, showed that PD/AW was associated with increased drug-use days during the follow-up period (B = .06, SE = .02, p < .001) but was unrelated to abstinence at follow-up, B = .23, SE = .35, p = .52. Not surprisingly, baseline drug use was associated with increased follow-up drug-use days (B = .06, SE = .00, p < .001) and a marginal decrease in likelihood of abstinence at follow-up (B = −.49, SE = .26, p = .06). Days in restricted environments during follow-up were unrelated to abstinence at follow-up (B = .02, SE = .08, p = .78) but were marginally associated with increased follow-up drug-use days, B = .01, SE = .01, p = .06.
TD/AW as a Moderator of the Association Between PD/AW and IP Drug-Use Days at Follow-Up
A ZIP analysis that controlled for baseline drug-use days, days spent in restricted environments at baseline and follow-up, and therapists’ years of counseling experience showed that PD/AW and TD/AW interacted to predict drug-use days at follow-up (B = .35, SE = .05, p < .001). Figure 1 illustrates the pattern of this interaction and Table 4 contains the full set of results. As shown in Figure 1, for IPs with relatively high levels of PD/AW at baseline (e.g., IPs whose level of PD/AW was at least 1/2 SD above the mean), as TD/AW increased, the IPs’ follow-up drug-use days increased, B = .34, SE = .05, p < .001. In contrast, for IPs who engaged in low levels of PD/AW (e.g., ≤1/2 SD below the average level of PD/AW), TD/AW was not associated with drug-use days during follow-up, B = −.01, SE = .05, p = .87.
Figure 1. Adolescents’ predicted number of drug-use days in the 7- to 12-month follow-up period as a function of baseline level of parent-demand/adolescent-withdraw interaction and level of therapist-demand/adolescent-withdraw in BSFT sessions.
Results of Zero-Inflated Poisson Regression Examining Predictors of Drug-Use Days and Complete Abstinence From T7 Through T12
DiscussionThis study attempted to evaluate a parallel-process hypothesis about DW interaction in family therapy for adolescent drug use. Specifically, we hypothesized that therapists’ demands for change, when linked with concomitant adolescent withdrawal, would predict poor substance-use outcomes in the context of established PD/AW family interaction. Results supported this hypothesis. TD/AW interacted with PD/AW such that, for families that engaged in a relatively high amount of PD/AW at baseline, TD/AW predicted increased IP drug-use days at follow-up, whereas for families low in PD/AW, TD/AW was unrelated to IP drug use. A separate analysis also showed that baseline PD/AW was associated with increases in IP drug use at follow-up. Interestingly, however, baseline PD/AW was marginally associated with increased likelihood of IP abstinence at baseline.
Our parallel DW process hypothesis was based on the assumption that PD/AW would act as a problem-maintaining interactional process associated with increases in IP drug use. One study has shown PD/AW to be associated with increases in adolescent drug use (Caughlin & Malis, 2004b) and several studies have found that DW interaction is associated with relationship dissatisfaction in couples (cf. Eldridge & Christensen, 2002). The current study findings agree with and build upon these findings. Whereas Caughlin and Malis (2004a, 2004b) found that PD/AW was cross-sectionally associated with increased adolescent drug use, we found that PD/AW predicted future increases in adolescent drug use. In addition, previous research that our group conducted using the same sample as the one the current study used showed that the PD/AW pattern was positively associated with observations of disengagement in the parent–adolescent dyads at baseline (Rohrbaugh et al., 2007). Disengagement is characterized by a lack of connectedness, empathy, and emotional support between the parent and adolescent, as well as impermeable boundaries, emotional distance, and absence of communication. Studies have shown that disengagement is associated with increases in adolescent drug use as well as internalizing behavior problems (Baumrind, 1991; Brook, Brook, Gordon, & Whiteman, 1990). The association between PD/AW and parent–adolescent disengagement and the fact that both PD/AW and family disengagement are associated with increased adolescent drug use supports the notion that PD/AW is a problematic interaction pattern in families of adolescent drug abusers.
Despite the fact that PD/AW predicted increases in follow-up drug use and increased parent–adolescent disengagement, PD/AW was marginally associated with increased abstinence at baseline. There may be a couple of explanations for this result. First, the level of PD/AW in this sample was low. In fact, the average rating of PD/AW was 1.86 (SD = .99) on a 1–5 scale, and only 10% of families had more than moderate (e.g., ratings higher than 3) levels of PD/AW. These statistics suggest that, at baseline, pronounced, repetitive PD/AW cycles were rare in the study families. Thus, baseline PD/AW may not have been at a level where it was a problem-maintenance process for most study families. The severity of the drug use of adolescents in the current study may also help explain why PD/AW was not associated with increases in adolescent drug use at baseline. Only one study to date has found an association between PD/AW and increased adolescent drug use and it used a sample of normal (nonclinical) adolescents who had low levels of drug use (Caughlin & Malis, 2004b). Indeed, on a 4-point scale with 1 indicating no recent alcohol or drug use and 4 indicating at least 10 episodes of use, adolescents’ average score was 1.21 (SD = .45). In comparison, adolescents in the current study used drugs on an average of eight days per month at baseline. Given these differences, it may be possible that PD/AW plays a different role in the drug use of adolescents with diagnosed substance abuse than it does in adolescents who infrequently use drugs. For adolescents who do not often use drugs, the amount of increase in PD/AW that is associated with increases in drug use may not serve to predict significant increases in drug use of adolescents with more severe drug-use problems.
The finding that PD/AW was marginally associated with increased abstinence at baseline might lead one to think that PD/AW was somehow associated with positive parenting practices that might have helped some adolescents stay abstinent from drugs. However, study findings suggest that this was unlikely. Indeed, we found that PD/AW was not associated with the positive discipline practices used in parental monitoring. Parental monitoring consists of paying attention to and tracking a child’s whereabouts, activities, and adaptations (Dishion & McMahon, 1998). Studies have shown that it is negatively associated with the development of both substance use (Dishion et al., 1998; Dishion & Loeber, 1985; Steinberg, 1986) and antisocial behavior (Patterson & Stouthamer-Loeber, 1984) in adolescents referred to treatment for these behaviors. The lack of association between parental monitoring and PD/AW suggests that PD/AW is a communication pattern that operates independently of the positive disciplinary practices that are involved in parental monitoring.
Interestingly, even though PD/AW was not significantly associated with adolescent drug use at baseline, it did interact with TD/AW to predict greater drug use at follow-up. Viewing DW interaction from a family-systems perspective may help explain this finding. In family systems theory (Rohrbaugh & Shoham, 2001; Watzlawick, Bavelas, & Jackson, 1967), DW interaction is conceptualized as a cyclical process in which demand and withdraw behaviors reinforce one another via positive feedback. Increased demands lead to increased withdrawal, which in turn feeds more demand. In the current study, the parallel DW processes in the parent–adolescent and therapist–adolescent dyads may have reciprocally reinforced one another. Increased therapist demands (e.g., telling the adolescent that drug use is dangerous, or that talking back is disrespectful) may have been associated with adolescent withdrawal, which in turn may have increased demands of the parent on the adolescent (e.g., “don’t be disrespectful”). In this way, TD/AW may have escalated the overall amount of DW in the family therapeutic system to a level where it became a problematic cycle associated with increases in the adolescent’s presenting drug-use problems. This may indicate that mirroring PD/AW by engaging in TD/AW interaction, even when the PD/AW cycle is not yet entrenched and problematic, could have the effect of contributing to poor adolescent drug-use outcomes. Alternatively, individual difference factors may have contributed to the study results. For example, an earlier finding from our group showed that, in this study sample, BSFT therapists tended to respond to adolescents who had relatively high baseline levels of substance abuse and conduct problems (e.g., histories of multiple arrests) with off-model behaviors such as didactic and prescriptive (e.g., “demanding”) interventions (Lebensohn-Chialvo, Hasler, Rohrbaugh, & Shoham, 2010). This result seemed to suggest that adolescents with more severe drug-use and behavior problems at baseline “pulled” off-model demanding and directive therapist behaviors. These behaviors in turn were correlated with poor overall fidelity to BSFT. These findings may help explain how parallel DW cycles formed in the current study. That is, as the drug use of adolescents involved in PD/AW worsened, this increased drug-use severity may have made it more likely that therapists would shift off-model to using a didactic, demanding stance in relation to the adolescent.
Other studies conducted from an individual-differences perspective may also help explain the study results. For example, it may be possible that IPs who engaged in PD/AW were particularly sensitive to therapist demands and unwilling to comply with them. This sensitivity, or reactance, may have lessened the effectiveness of BSFT for these IPs. Previous research has shown that client resistance in family therapy is associated with low therapist ratings of outcome (Chamberlain, Patterson, Reid, Kavanagh, & Forgatch, 1984), and in alcohol-related interventions, increased alcohol use one year following treatment (Miller, Benefield, & Tonigan, 1993). In addition, studies have shown that interactions between therapist directiveness and client reactance predict negative outcomes of alcohol-related interventions (Karno, Beutler, & Harwood, 2002; Karno & Longabaugh, 2005). These studies indicated that, for highly reactant clients, therapist directiveness was associated with increased posttreatment alcohol use, whereas low-reactance clients either had a better drinking outcome after following therapists’ directives (Karno et al., 2002) or their alcohol use was not affected by therapist directiveness (Karno & Longabaugh, 2005). From the perspective of this research, the individual traits of IP reactance and therapist directiveness, rather than isomorphic DW interaction dynamics, may explain why PD/AW interacted with TD/AW to predict IP drug use.
The current study had some limitations. First, the study sample consisted of a subset of participants from the parent study that were not selected at random. Although there were no demographic differences between this group and the adolescents that were excluded, the included participants attended significantly more BSFT sessions than did the excluded participants. Thus, our findings may not generalize to samples that receive lower levels of treatment or drop out early from treatment. A second limitation pertains to the correlational nature of the study. Therapists were not randomized to “low-demand” and “high-demand” conditions. Thus, third variables such as therapist behaviors correlated with TD/AW may have contributed to our results. A third factor that may limit the generalizability of findings concerns the trainee status of the study therapists. The demand behaviors we observed in the study therapists may be specific to their developmental stage as new BSFT trainees. It is quite possible that more experienced BSFT therapists would interact differently with family clients and that our results would not replicate with these therapists. Lastly, the study results occurred in a sample of adolescents whose number of drug-use days did not change significantly from baseline to follow-up, and whose abstinence rates decreased from baseline to follow-up. Thus, the effects of PD/AW and TD/AW may differ in samples that decrease their drug use over time.
In sum, results suggest that parallel DW processes in family therapy for adolescent drug abuse can compromise treatment outcome. Findings highlight two key BSFT principles—remaining decentralized and placing more demand for change on parents than on adolescents—and suggest that these principles are particularly important to observe when therapists work with families that exhibit PD/AW. More research is needed to discern the role that systemic DW processes versus individual traits such as therapist directiveness and adolescent reactance play in predicting outcomes of family therapy. For example, future research could evaluate whether instances of TD/AW interaction reliably predict subsequent PD/AW, which then predict increased TD/AW (e.g., a therapist tells the adolescent that drug use is dangerous, the adolescent withdraws, and the parent then tells the adolescent to listen to the therapist). Coding such sequences of DW communication may provide a more purely systemic measure of parallel DW interactions than the one used in the current study. Using such a measure, researchers could compare coded DW sequences with observations of therapist directiveness and adolescent reactance to test the degree to which each measure predicts adolescent drug-use outcomes. Such research would provide concrete information to family therapists regarding the degree to which demand–withdraw processes versus adolescent reactance and therapist directiveness traits predict poor outcome. Information provided by this type of research would likely be particularly beneficial to family therapy trainees and supervisors seeking to avoid getting involved in maladaptive interaction processes with the family. It would also help improve family interventions by clarifying processes or traits that may contribute to poor therapy outcomes.
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Submitted: July 23, 2012 Revised: December 17, 2012 Accepted: December 18, 2012
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Record: 38- Parental alcohol involvement and adolescent alcohol expectancies predict alcohol involvement in male adolescents. Cranford, James A.; Zucker, Robert A.; Jester, Jennifer M.; Puttler, Leon I.; Fitzgerald, Hiram E.; Psychology of Addictive Behaviors, Vol 24(3), Sep, 2010 pp. 386-396. Publisher: American Psychological Association; [Journal Article] Abstract: Current models of adolescent drinking behavior hypothesize that alcohol expectancies mediate the effects of other proximal and distal risk factors. This longitudinal study tested the hypothesis that the effects of parental alcohol involvement on their children's drinking behavior in mid-adolescence are mediated by the children's alcohol expectancies in early adolescence. A sample of 148 initially 9–11 year old boys and their parents from a high-risk population and a contrast group of community families completed measures of drinking behavior and alcohol expectancies over a 6-year interval. We analyzed data from middle childhood (M age = 10.4 years), early adolescence (M age = 13.5 years), and mid-adolescence (M age = 16.5 years). The sample was restricted only to adolescents who had begun to drink by mid-adolescence. Results from zero-inflated Poisson regression analyses showed that 1) maternal drinking during their children's middle childhood predicted number of drinking days in middle adolescence; 2) negative and positive alcohol expectancies in early adolescence predicted odds of any intoxication in middle adolescence; and 3) paternal alcoholism during their children's middle childhood and adolescents' alcohol expectancies in early adolescence predicted frequency of intoxication in middle adolescence. Contrary to predictions, child alcohol expectancies did not mediate the effects of parental alcohol involvement in this high-risk sample. Different aspects of parental alcohol involvement, along with early adolescent alcohol expectancies, independently predicted adolescent drinking behavior in middle adolescence. Alternative pathways for the influence of maternal and paternal alcohol involvement and implications for expectancy models of adolescent drinking behavior were discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Parental Alcohol Involvement and Adolescent Alcohol Expectancies Predict Alcohol Involvement in Male Adolescents
By: James A. Cranford
Addiction Research Center, Department of Psychiatry, University of Michigan;
Robert A. Zucker
Addiction Research Center, Department of Psychiatry, University of Michigan
Jennifer M. Jester
Addiction Research Center, Department of Psychiatry, University of Michigan
Leon I. Puttler
Addiction Research Center, Department of Psychiatry, University of Michigan
Hiram E. Fitzgerald
University Outreach and Engagement, Kellogg Center, Michigan State University
Acknowledgement: This work was supported by Grants T32 AA07477 and Grant R37 AA07065 from the National Institute on Alcohol Abuse and Alcoholism to Robert A. Zucker, PhD. We thank the families who participate in the Michigan Longitudinal Study and Susan K. Refior, whose sustained work with the families in this study has been a major contributor to the study's continuation. We also thank the editor and the anonymous reviewers for helpful comments on previous drafts of this manuscript.
Does exposure to parental drinking influence the drinking behavior of their children once drinking onset has occurred? If so, are the effects mediated by childhood alcohol expectancies (AEs) preceding the child's drinking? A probabilistic-developmental model of risk ( Zucker, 1994; Zucker, Fitzgerald, & Moses, 1995; Zucker, Donovan, Masten, Mattson, & Moss, 2008) emphasizes the cumulation of risk factors over time for the developmental course of problem drinking, and the specification of how various risk factors work together to produce negative outcomes has been identified as a priority topic (Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001). In this paper, we examine the longitudinal effects of parental alcohol involvement and adolescent AEs on subsequent alcohol involvement among adolescents who had begun drinking by mid-adolescence. Below, we review the literature on parental alcohol involvement and adolescent AEs on later drinking behavior.
Effects of Parental Alcohol Involvement on Adolescent Drinking BehaviorAn extensive body of evidence showed that parental substance use and family history of substance use are predictive of adolescent substance use (for reviews, see Ellis, Zucker, & Fitzgerald, 1997; Hawkins, Catalano, & Miller, 1992; Scheier, 2001; Sher, Grekin, & Williams, 2005; Wills & Yaeger, 2003; Windle, 1996). For example, Sher, Walitzer, Wood, and Brent (1991) examined the concurrent effects of parental alcoholism on adolescent alcohol involvement in a large sample of college freshmen (M age = 18.2 years). They found that children of alcoholics (COAs) reported higher levels of alcohol involvement (e.g., quantity-frequency of consumption, heavy drinking, and alcohol dependence symptoms) compared to non-COAs, although the extent to which alcoholic parents were actually drinking during the child's earlier years was not evaluated. Similar findings have been obtained with samples of younger adolescents. For example, working with a sample of alcoholic and nonalcoholic families from the community, Chassin, Rogosch, and Barrera (1991) examined the concurrent effects of parental alcoholism on the alcohol involvement of the adolescent offspring (M age = 12.7). Results showed that paternal but not maternal alcoholism predicted greater adolescent alcohol involvement, and this effect was stronger among the older adolescents.
Other evidence in addition to the Chassin et al. (1991) study indicated that the association between parental and offspring alcohol involvement differs depending on the gender of the parent and/or the child. However, the nature of the relationship has not been consistently replicated. Thus, Zhang, Welte, and Wieczorek (1999) found that paternal but not maternal drinking had a direct concurrent effect on adolescent boys' drinking (age range 16–19). By contrast, Ohannessian et al. (2004) found that maternal substance use consequences were more consistently related to concurrent adolescent psychopathology (including alcohol dependence, depression, and conduct disorder) than paternal substance use consequences. Furthermore, parental effects are not always observed; in another cross-sectional study, Cooper, Peirce, and Tidwell (1995) found no consistent associations between maternal or paternal drinking and adolescent substance use. Similarly, Yu (2003) showed that parental alcohol use was related to lifetime but not current alcohol use of their adolescent children (age range 15–18).
Although smaller in number, longitudinal studies have yielded similar results. White, Johnson, and Buyske (2000) followed adolescents across four waves from ages 15 to 28. Paternal and maternal drinking were predictive of a heavy drinking trajectory among sons and daughters. Wills, Sandy, Yaeger, and Shinar (2001) assessed 1,269 adolescents in 6th, 7th, and 8th grade and found that parental substance use (alcohol and tobacco use as reported by the child) predicted higher adolescent substance use (a latent variable comprised of alcohol, tobacco, and marijuana use) in grade 6, but did not predict increases in adolescent use over time. In one of the few longitudinal studies utilizing a high-risk sample, Chassin, Curran, Hussong, and Colder (1996) found that paternal and maternal alcoholism predicted initial levels of substance use (alcohol and illicit drug use) among girls and boys, but only paternal alcoholism and adolescent male gender predicted increases in substance use over a 3-year interval. Although the effects of paternal alcoholism were partially mediated by fathers' monitoring and adolescents' stress, negative affect, and associations with substance-using peers, these hypothesized mediators did not fully account for the paternal alcoholism effect.
Effects of Child and Adolescent AEs on Adolescent Drinking BehaviorAEs are another potent risk factor for adolescent alcohol involvement. The available evidence indicates that alcohol expectancies are “among the strongest predictors of drinking, even after other variables are controlled” ( Goldman, Del Boca, & Darkes, 1999, p. 219). Previous research using school-based samples showed that AEs predicted drinking onset among adolescents (Bauman, Fisher, Bryan, & Chenoweth, 1985; Killen et al., 1996), and results from several longitudinal studies found that adolescent AEs predicted increases in alcohol consumption over time (e.g., Aas, Leigh, Anderssen, & Jakobsen, 1998; Bauman et al., 1985; Christiansen, Smith, Roehling, & Goldman, 1989; Newcomb, Chou, Bentler, & Huba, 1988; Smith, Goldman, Greenbaum, & Christiansen, 1995). Similar findings have been obtained from longitudinal studies of college student samples (Darkes, Greenbaum, & Goldman, 2004; Goldman, Greenbaum, & Darkes, 1997; Kidorf, Sherman, Johnson, & Bigelow, 1995; Sher, Wood, Wood, & Raskin, 1996). In addition, there is longitudinal evidence that AEs predict the transition from nonproblem to problem drinking (Christiansen et al., 1989), and a recent study of high school students (M age = 16.2) found that tension-reduction AEs were concurrently associated with frequency of drunkenness (Catanzaro & Laurent, 2004).
Child and Adolescent AEs as Mediators of the Effects of Parental Drinking on Adolescent DrinkingTo this point, our review has indicated some linkages between a) parental and adolescent drinking, and b) adolescent AEs and adolescent drinking. Models of AEs emphasize their role as mediators of the effects of more distal risk factors ( Goldman et al., 1999; Petraitis, Flay, & Miller, 1995). Tests of social learning theory explanations of adolescent alcohol use have shown that exposure to parents who use alcohol has a direct relationship to AEs (Zucker, Kincaid, Fitzgerald, & Bingham, 1995), which in turn predicts alcohol involvement (Petraitis et al). The hypothesized mediational effects of AEs have been elaborated by a number of alcohol researchers (Goldman et al., 1999; Scheier & Botvin, 1997; Sher et al., 1991).
However, direct tests of the mediational role of AEs, based on the assumption that AEs are among the most proximal correlates of drinking behavior, have yielded conflicting findings. For example, the cross-sectional study by Sher et al. (1991) found that the effect of family history of paternal alcoholism on college students' alcohol involvement was mediated by the students' own level of behavioral under-control as well as by their (positive) AE level. Ouellette, Gerrard, Gibbons, and Reis-Bergan (1999) followed parents and their offspring over 4 years and showed that the effect of parental drinking (average of maternal and paternal consumption) on adolescent alcohol consumption was mediated by adolescent AEs. By contrast, a longitudinal study by Colder, Chassin, Stice, and Curran (1997) found that parental alcoholism had a direct effect on increases in adolescent heavy drinking that was not mediated by the adolescents' AEs.
The Present StudyTaken together, the available evidence remains unclear with respect to the hypothesis that adolescent AEs mediate the effects of their parents' alcohol involvement on their own drinking during adolescence. Although evidence suggests linkages between a) maternal and paternal alcohol involvement and their adolescent children's drinking and b) the adolescent's AEs on their own drinking, longitudinal studies have not produced consistent support for the mediational hypothesis. A major challenge to understanding these relationships is that both drinking and nondrinking adolescents have typically been included in the same analyses even though evidence has indicated that AEs change substantially as a function of the transition from nondrinking to drinking (see Christiansen, Goldman, & Inn, 1982; Schell, Martino, Ellickson, Collins, & McCaffrey, 2005). Such changes have been linked to the concrete experience of drinking and the exposure to alcohol's pharmacodynamic effects (Aas et al., 1998). Thus, inclusion of both groups creates a confounded predictor (the expectancies). In this paper, we examine the possible mediational role of childhood AEs in explaining the association between parental alcohol involvement and adolescent drinking behavior once drinking has begun (i.e., after the transition has occurred). Using longitudinal data, we tested the hypothesis that the association between parental drinking and adolescent drinking is mediated by adolescents' AEs.
Method Participants
The present work is from the ongoing Michigan Longitudinal Study (MLS; Zucker et al., 2000), a prospective study that is following a community sample of initially intact families with high levels of substance use/abuse, along with a community contrast sample of families drawn from the same neighborhoods, but without the high substance abuse profile. The long term focus of the project is the emergence and development of substance abuse and problems in the children, and the patterns of stability and change in drug involvement among the parents.
A community-based, but high alcohol-involved, sample of initially intact families was recruited by identifying fathers on the basis of a drunk driving conviction with a high blood alcohol level (0.15 percent if a first conviction, 0.12 percent if not the first). Families were required to have at least one son in the 3–5 year age range, and daughters in the 3–11 year age range were recruited when present. Presence of fetal alcohol syndrome was ruled out by study exclusionary criteria. Both biological parents were required to be living with the child at the time of recruitment, and mothers' substance use status was free to vary. A contrast/control group of families who resided in the same neighborhoods as the drunk driver families but had no substance abuse history for either parent was also recruited. A second subset of families with a father who also had an alcohol use disorder was uncovered and recruited during the community canvass for controls ( Zucker et al., 2000). After initial recruitment and assessment, individuals participated in multi-session assessments every 3 years. Data collection was completed by professional staff, graduate students, and carefully trained and supervised undergraduates.
Participants for the present study were biological fathers, mothers, and sons who completed relevant measures at child ages 9–11 (middle childhood), 12–14 (early adolescence), and 15–17 (mid-adolescence). For ease of presentation, we refer to these time points as baseline (T1), T2, and T3 (although the MLS designation is T3, T4, and T5). A total of 259 MLS families completed the study protocol at T3. This total included a subset of girls potentially available for the study. However, the sample of girls available for this study (n = 21) was not large enough to conduct meaningful analyses by gender, and because substantial sex differences are known to exist for many drinking indicators, we limited our analyses to boys. Also, because the present work is focused on individual differences in later adolescence, when drinking has to a large extent been initiated, we analyzed data only from families where the boys had begun drinking by T3 (when they were an average of 16.5 years old). Of the 259 boys who completed the T3 protocol, 57.1% (N = 148) had begun drinking. We analyzed data from these 148 boys and their parents at T1, T2, and T3. Based on responses to the question “How old were you the first time you ever took a drink? Do not count the times you were given a 'sip' by an adult,” the mean (SD) of drinking onset was 13.5 (2.5) years old (range = 5 to 17 years old). Although the sample of 148 boys included 5 male siblings of the male target children (MTCs), all participants were treated as independent based on an intraclass correlation of 0. At baseline, T2, and T3, average ages for children (with standard deviations in parentheses) were 10.4 (.9), 13.5 (.9), and 16.5 (1.0) years.
Parents at T1 were 148 mothers and fathers whose mean (SD) ages were 36.6 (4.0), and 39.2 (5.0) years, respectively. Couples had been married for an average of 11.4 years. Both couple members had completed about 2 years of education beyond high school: mean total education years for mothers, M (SD) = 14.2 (1.9); for fathers, M = 14.9 (2.3), and median family income was $40,000. All families were Caucasian because less than 4% of the population we sampled from was non-Caucasian. Given our sample size, this precluded effective analyses of race and ethnic differences.
Measures
Parental lifetime alcohol use disorder (AUD) and alcohol involvement at T1
DSM-IV alcoholism diagnosis for both parents was assessed using several measures, including the Short Michigan Alcoholism Screening Test (SMAST; Selzer, Vinokur, & van Rooijen, 1975), the Drinking and Drug History Questionnaire (DDH; Zucker, 1991), and the Diagnostic Interview Schedule—Version IV (DIS-IV; Robins, Helzer, Croughan, & Ratcliff, 1981). The SMAST is a 13-item self-report screening inventory that assesses alcohol problems. The DDH contains a series of questions asking about alcohol and other drug use and alcohol-related consequences over the past 6 months. The DIS-IV is a structured diagnostic interview that collects extensive information about physical, alcohol- and drug-related symptoms, and other psychiatric symptoms. Trained clinicians used data from all three sources of data to create a best-estimate diagnosis (Leckman, Sholomskas, Thompson, Belanger, & Weisman, 1982) of a lifetime alcohol use disorder (abuse or dependence) for both parents. DIS data were used as the base supplemented by the DDH and SMAST data, guided by the principle that when a symptom was admitted, even from only one source, it probably was present. To evaluate the reliability of this pooled diagnosis, two raters independently diagnosed a series of 26 protocols. Agreement as evaluated by kappa was .81, indicating acceptable reliability. In this subsample of N = 148 drinking boys, 33.1% (n = 49) of the mothers and 76.4% (n = 113) of the fathers had a lifetime AUD. In terms of family risk status, 23.7% (n = 35) of the families were control families; 18.9% (n = 28) were families from the community in which the father had an AUD; and 57.4% (n = 85) were families in which the father had a drunk driving conviction.
Less severe forms of parental alcohol involvement (e.g., frequency of alcohol consumption; Ary, Tildesley, Hops, & Andrews, 1993) have also shown longitudinal associations with adolescent drinking. Accordingly, we included a measure of average number of drinking days per month in the last 6 months from the DDH (Zucker, 1991). The average number of drinking days per month was lower for mothers (M = 4.1, SD = 5.6) than fathers (M = 8.6, SD = 8.5), paired t(135) = −6.2, p < .01.
Alcohol expectancies at T2
AEs were assessed with the Beverage Opinion Questionnaire (BOQ; Fitzgerald, Zucker, & Noll, 1990) which was administered to participants starting when they were between the ages of 6 and 8 years and then again at T1, T2, and T3. This 25-item questionnaire assesses negative (5 items) and positive (20 items) expectancies for alcohol and, as a buffer, also includes 30 expectancy questions about soft drinks. The BOQ is based on the adolescent version of the Alcohol Expectancy Questionnaire (AEQ-A; Christiansen et al., 1982; Brown, Christiansen, & Goldman, 1987) and the adult version of the Alcohol Expectancy Questionnaire (AEQ; Brown, Goldman, Inn, & Anderson, 1980). The original version of the AEQ-A was developed for use with adolescents ages 12–19 (Christiansen et al., 1982) and consists of 90 items. To reduce participant burden we sought to reduce the number of items for inclusion in the BOQ. Further, because we began asking about AEs when participants were between the ages of 6 and 8 years old, we selected those items that seemed likely to be most comprehensible when read to children by the interviewers. With these concerns in mind, we selected 23 items from the AEQ-A. In addition, we selected two items from the adult AEQ that focused on sleep. Each statement concerning alcohol is in the format “Drinking beer or wine would…”, followed by a phrase indicating an expectancy for alcohol, e.g., “Drinking beer or wine would make me feel good” (positive expectancy), “Drinking beer or wine would make me feel angry” (negative expectancy). Adolescents were asked to respond to each item on a four-point scale (1 = agree completely, 2 = somewhat agree, 3 = somewhat disagree, 4 = completely disagree). Inspection of the item distributions showed that few participants selected the “somewhat agree” or “somewhat disagree” response options. Thus, we collapsed response options to create binary versions of each item, where 0 = disagree completely or somewhat disagree and 1 = agree completely or somewhat agree. Items were summed to create negative and positive expectancies scores for each participant. At T2, the positive AEs scale had an alpha of .88 (M = 2.4, SD = 3.6), the negative AEs scale had an alpha of .74 (M = 1.9, SD = 1.4), and the two scales were moderately correlated, r = .44, p < .01.
Adolescent alcohol involvement at T3
For the adolescent version of the DDH, participants were asked about average drinking days per month (drinking days) over the past 6 months at T3 (M = 2.8, SD = 3.8). In other words, adolescents reported drinking on approximately 18 days during the past 6 months. Adolescent participants at T3 were also asked to indicate how many times during the past 6 months they had gotten drunk or very high from drinking alcohol. Scores on this variable ranged from 0 (not at all) to 52 (about twice a week) with a mean (SD) of 9.8 (20.4) episodes of drunkenness during the past 6 months.
Analytic Plan
Correlation and regression analyses were used to test the study hypotheses. Our two dependent variables (number of drinking days and number of drunken episodes in the past 6 months) were count variables. Count variables can sometimes be modeled as Poisson variables, but the Poisson distribution is restricted to a single parameter for the mean and the variance, and alcohol-related count variables often exceed this restriction by having larger variances than means (i.e., overdispersion; Horton, Kim, & Saitz, 2007). Although the present sample consisted only of those who had started drinking, a substantial percentage of participants reported that they did not drink or experience any drunken episodes in the past 6 months (29.1% and 34.5% of the sample, respectively). Count variables with large numbers of zeroes are referred to as “zero-inflated” (Karaszia & van Dulmen, 2008). Accordingly, zero-inflated Poisson (ZIP) regression analysis (Lambert, 1992) was used to examine predictors of drinking behaviors. For ZIP models, two regression equations are estimated simultaneously: 1) a logistic regression model is used to predict whether or not a given behavior occurs (i.e. membership in an “always zero” versus a “not always zero” latent group; Karazsia & van Dulmen, 2008), and 2) a Poisson regression model is used to predict the number of times a given behavior occurs (Atkins & Gallop, 2007; Muthen & Muthen, 2007). Estimating ZIP models thus allows for the possibility that different variables may predict whether or not someone drinks and how much or how often someone drinks (Atkins & Gallop, 2007). All ZIP models were estimated with the Mplus software package using maximum likelihood estimation with robust standard errors (Muthen & Muthen, 2007). For ease of interpretation of the logistic regression results, we report the reciprocal odds ratio for each predictor so that they represent the odds of being in the nonzero class, i.e. the odds of the occurrence of each drinking behavior.
Missing data
Because the pairwise sample sizes for the variables in our models ranged from n = 87 to n = 148, we used multiple imputation (MI; Rubin, 1987; Sinharay, Stern, & Russell, 2001) to impute missing data. In MI, each missing value is replaced by m > 1 simulated values, resulting in m complete data sets (Schafer, 1997; Schafer & Graham, 2002). These m data sets are analyzed using standard analytic methods, and the results are combined to obtain parameter estimates and standard errors that take into account missing data uncertainty (Sinharay et al., 2001). MI is based on the assumption that the data are missing at random (MAR; Sinharay et al., 2001). Since this assumption is not testable, we included several variables in the imputation model that could potentially be linked to the missingness of the imputed variables (Schafer, 1997; Sinharay et al., 2001).
The pattern of missing data appeared to be arbitrary, and so we used the Markov Chain Monte Carlo (MCMC) imputation method ( Schafer, 1997). For the variables in the models we tested, the rate of missing information (λ) ranged from a low of .15 to a high of .48. Results from a simulation study (Graham, Olchowski, & Gilreath, 2007) showed with m = 10 imputations: 1) the power to detect a small effect size when λ = .50 showed a decrease of only about 3%, compared to simulations with m = 100 imputations; and 2) the relative efficiency of a given parameter estimate when λ = .50 is .96 (compared to simulations with m = 100 imputations). Accordingly, we used SAS PROC MI (SAS, 2004) to create m = 10 imputed data sets. We then used PROC CORR to conduct correlational analyses on the m = 10 imputed data sets, and PROC MIANALYZE to combine the results from analyses of the m = 10 data sets. As noted earlier, for the ZIP models, we used the Mplus program with multiple imputation and maximum likelihood estimation with robust standard errors (Muthen & Muthen, 2007).
Attrition analyses
For mothers and fathers, we compared T1 responders and nonresponders on the measure of drinking frequency at T3, and no significant differences between responders and nonresponders were observed. For adolescents, we compared T2 responders and nonresponders on the measures of positive and negative AEs at T3, and no significant differences between responders and nonresponders were observed. These results indicate that any nonresponse bias was minimal.
ResultsCorrelations between all study variables are presented in Table 1. Maternal and paternal measures of AUD and drinking frequency were moderately correlated (rs ranged from .22 to .33). Interestingly, there was no significant association between paternal AUD and maternal drinking frequency, or between maternal AUD and paternal drinking frequency. No statistically significant correlations between parental alcohol involvement and adolescents' positive AEs were observed, but there was a weak direct association between paternal AUD and adolescents' negative AEs 3 years later at T2. Paternal AUD and maternal drinking frequency were significantly associated with both measures of adolescent alcohol involvement. Maternal AUD was also positively associated with adolescents' drinking frequency (but not frequency of drunkenness), and paternal drinking frequency was positively associated with adolescents' frequency of drunkenness (but not drinking frequency). Adolescents' positive AEs at T2 were positively related to frequency of drunkenness (but not drinking frequency) at T3. Adolescents' negative AEs at T2 were not significantly associated with either measure of adolescent alcohol involvement.
Zero-Order Correlations Between Study Variables
Baron and Kenny (1986) outline methods for testing mediational hypotheses. These steps include 1) establishing an association between the predictor and the outcome variable; 2) establishing an association between the predictor and the putative mediator variable; 3) establishing an association between mediator and the outcome variable when the predictor is statistically controlled; and 4) showing that the association between the predictor and the outcome is reduced in magnitude when the mediator variable is entered into the regression equation. Although demonstration of a relationship between the predictor and the outcome is not always required (Shrout & Bolger, 2002), an association between the predictor and the putative mediator variable is necessary to establish mediation. However, as seen in Table 1, only one of the measures of parental alcohol involvement was associated with adolescent AEs: paternal AUD showed a direct association with negative AEs. Further, negative AEs were not associated with either measure of adolescent alcohol involvement. These findings do not support the hypothesis that adolescent AEs mediate the effects of parental alcohol involvement on adolescent drinking.
We then examined the effects of T1 parental alcohol involvement and T2 adolescent AEs as independent predictors of adolescent drinking at T3. ZIP regression analyses were conducted using the Mplus statistical software program ( Muthen & Muthen, 2007). Results are presented in Table 2. We first tested the effects of lifetime paternal and maternal AUD, T1 paternal and maternal alcohol involvement, and T2 adolescent AEs as predictors of average drinking days in the past 6 months at T3. Because there was variation in age within waves, we controlled for adolescents' age at T3 in all analyses. For the logistic regression of the binary part of the dependent variable, age was the only significant predictor, and every 1 unit increase in age resulted in a 2.05 increase in the odds of drinking on any days in the past 6 months. For the Poisson regression of the count part of the dependent variable, the only significant predictor was T1 maternal drinking. To gain perspective on the meaning of the Poisson coefficients, we used procedures outlined by Long (1997, p. 229). For each 1-unit increase in mothers' average drinking days per month, adolescents' drinking days per month increased by a factor of 1.033, an increase of 3.3%, when all other predictors were statistically controlled. Although significant, this appears to be a relatively small effect. For example, at the average level of mothers' average drinking days per month (4.3), the expected number of adolescents' drinking days is 3.2. At one standard deviation above the average level of mothers' average drinking days per month (10.7), the expected number of adolescents' drinking days is 4.0.
Zero-Inflated Poisson Regression Analysis of Longitudinal Predictors of Adolescents' Average Number of Drinking Days per Month and Number of Times Intoxicated in Past 6 Months
Next, we tested the effects of lifetime paternal and maternal AUD, T1 paternal and maternal alcohol involvement, and T2 adolescent AEs as predictors of frequency of intoxication in the past 6 months at T3. As seen in Table 2, for the logistic regression of the binary part of the dependent variable, age and negative and positive AEs were significant predictors of any intoxication in the past 6 months. Increases in age and positive expectancies resulted in higher odds of any intoxication, and increases in negative expectancies resulted in lower odds of any intoxication. None of the maternal or paternal alcohol involvement variables were associated with any intoxication in the past 6 months. For the Poisson regression of the count part of the dependent variable, lifetime paternal AUD and T2 positive expectancies were significantly associated with the frequency of intoxication in the past 6 months.
We again used procedures outlined by Long (1997) to gain perspective on the meaning of the Poisson coefficients. For each 1-unit increase in positive AEs at T2, adolescents' number of times intoxicated increased by a factor of exp(.069) = 1.07, an increase of 7.0%, when all other predictors were statistically controlled. Although significant, this appears to be a relatively small effect. For example, at the average level of positive AEs at T2 (2.4), the expected number of times intoxicated is 8.3. At one unit above the average level of positive AEs at T2 (3.4), the expected number of times intoxicated is 8.9. We also used procedures outlined by Long (1997) to calculate the additive change in number of times intoxicated for adolescents as a function of father's lifetime AUD. For adolescents with a non-AUD father, the expected number of times intoxicated in the past 6 months is 3.3. By contrast, for adolescents with an AUD father, the expected number of times intoxicated in the past 6 months is 11.0, holding all other variables constant. Thus, while lifetime paternal AUD and T2 positive expectancies were independently associated with increases in the number of times intoxicated in the past 6 months at T3, the effects of paternal lifetime AUD were particularly strong in magnitude.
DiscussionThis study tested the hypothesis that AEs mediate the effects of parental alcohol involvement on adolescent drinking behavior. In partial support of our hypotheses, we found that two aspects of parental alcohol involvement (i.e., paternal lifetime AUD and maternal average drinking days per month) during middle childhood (T1) predicted some dimensions of mid-adolescent drinking (T3). Contrary to our hypothesis, results showed that the effects of parental alcohol involvement were not mediated by adolescent AEs. Rather, parental drinking and positive and negative adolescent AEs had independent longitudinal associations with adolescent drinking behavior.
Parental Alcohol Involvement and Adolescents' Drinking Behaviors
Our findings with respect to the effects of parental alcohol involvement on adolescents' drinking behaviors are consistent with a long line of work indicating that parents have profound effects on the drinking behaviors of their children (e.g., Fitzgerald, Davies, & Zucker, 2002; Jacob & Johnson, 1997; Wills & Yaeger, 2003; Windle, 1996; Zucker et al., 2000, 2008). Our findings are unique, however, in showing that different aspects of paternal and maternal alcohol involvement are longitudinally associated with different aspects of their sons' alcohol involvement 6 years later. The finding that mothers' but not fathers' drinking behavior was predictive of subsequent alcohol involvement in their sons is consistent with the work of Brook and her colleagues (Brook, Whiteman, Gordon, & Cohen, 1986), who found that aspects of the mother-child relationship were stronger protective factors for adolescent drug use than were similar aspects of the father-child relationship. Brook et al. speculated that mothers have more influence on child-rearing practices than do fathers. Our findings indicate that this influence extends to the domain of alcohol involvement, at least in terms of adolescents' average drinking days per month (also see Christiansen & Goldman, 1983). Related to this point, the greater amount of time spent with mothers versus fathers may lead adolescents to more closely model their own drinking behavior after that of their mothers. This may in part explain why maternal drinking behavior, but not maternal AUD, predicted their son's drinking behavior (Ohannessian & Hesselbrock, 2004).
By contrast, paternal alcoholism (but not paternal drinking behavior) was predictive of sons' alcohol involvement, and this effect was limited to frequency of intoxication. Paternal alcoholism is associated with a wide range of parenting variables, including less parental discipline ( King & Chassin, 2004), lower levels of parental monitoring (Chassin, Pillow, Curran, Molina, & Barrera, 1993; Chassin et al., 1996), and higher levels of child abuse and neglect (Richter & Richter, 2001); all of these variables are separately associated with earlier and heavier drinking among offspring. Our findings are thus consistent with recent work showing that COAs continue to show elevated levels of heavy drinking even when their fathers' alcoholism has remitted (DeLucia, Belz, & Chassin, 2001). In addition, parental alcoholism confers heightened genetic risk among some COAs (Zucker et al., 2008). The combination of socialization and genetic risk may explain the relatively large magnitude of the effect of paternal alcoholism on adolescents' intoxication.
With respect to the null findings for paternal drinking behavior, we note that paternal as compared to maternal drinking is more likely to occur on a sporadic basis for antisocial alcoholics ( Jacob & Leonard, 1988), a group that has a substantial representation in the current sample of alcoholics. Thus, exposure to father drinking, especially during late preadolescence, would have been less available to the children than exposure to mother drinking. Last, to some degree the alcoholic fathers' drinking behavior was dampened during the earlier years of the study as a result of conviction for drunk driving. Such convictions sometimes required attendance at alcohol education classes and produced a dampening effect on fathers' consumption which would distort the relationship between their own undampened drinking and their children's alcohol involvement. This process was not in operation for the mothers.
The observation that paternal alcoholism—but not paternal drinking behavior—was predictive of sons' alcohol involvement is not readily attributable to a drinking-modeling explanation (see Sher et al., 2005). However, exposure to parental modeling was not directly measured in this study. Brown, Tate, Vik, Haas, & Aarons (1999) showed that degree of exposure to an alcohol-abusing family member mediated the association between parental alcoholism and positive AEs, and noted that variation in exposure to alcohol-abusing family members, even within families characterized by a biological history of alcoholism, might be considerable. In the absence of a direct measure of parental modeling, a drinking-modeling explanation for the present findings related to paternal AUD cannot be ruled out.
Adolescent AEs and Drinking Behaviors
Our results also showed that adolescents' negative and positive AEs were longitudinally associated with higher odds of any intoxication 3 years later, and positive AEs further predicted frequency of drunkenness, independently of parental alcohol involvement. These findings replicate previous work in showing that AEs in early adolescence are longitudinally associated with drinking later in adolescence ( Reese, Chassin, & Molina, 1994; Smith et al., 1995), and more generally with previous results showing that positive and negative expectancies are predictive of alcohol involvement (Goldman & Darkes, 2004). Further, the differential effects of negative and positive AEs on the occurrence and frequency of intoxication are consistent with evidence reported by Leigh and Stacy (2004), who suggested that “negative expectancy predicts abstention while positive expectancy predicts amount of drinking among those who drink” (p. 224) (also see Chen, Grube, & Madden, 1994).
Results are also consistent with the hypothesis advanced by Sher and Gotham (1999) that AEs are developmentally specific risk factors for alcohol involvement. Alcohol schemas emerge as early as age 3 (Zucker et al., 1995), and evidence indicated a shift in AEs from more negative to more positive during the period from middle childhood to early adolescence (grades 6 to 9; Dunn & Goldman, 1998, 2000; cf. Spiegler, 1983). Positive expectancies may better predict alcohol involvement than negative expectancies among younger participants, but the effects of negative AEs become stronger as a function of age (Leigh & Stacy, 2004). The current results add to this literature by showing that positive expectancies in middle adolescence have a stronger association to risky drinking than to overall frequency of drinking behavior.
Adolescent AEs as Mediators of the Effects of Parental Alcohol Involvement
Our findings did not support the hypothesis that the effects of parental alcohol involvement are mediated by AEs ( Chassin et al., 1996). However, it is important to consider some aspects of the current study that limited our ability to draw firm conclusions about the mediation hypothesis. Our sample was by design limited to Caucasian males, many of whom were living in high-risk families, and this limits the generality of the findings. Also, our relatively small sample size was underpowered to detect mediation when the associations between a) the independent variable and the dependent variable, and b) the mediator and the dependent variable are in the small to moderate range (Fritz & MacKinnon, 2007).
Our own, as well as other, work suggests a complex relationship between parental drinking behavior and AEs in children and adolescents. As noted earlier, several studies have found evidence for linkages between paternal alcohol involvement and their children's AEs (e.g., Brown et al., 1999). By contrast, Kraus, Smith, and Ratner (1994) found no cross-sectional associations between AEs among 268 children in grades 2 through 4 and maternal and paternal drinking attitudes, parental problem drinking, and family history of AUD (also see Brown, Creamer, & Stetson, 1987; Henderson, Goldman, Coovert, & Carnevalla, 1994; Miller, Smith, & Goldman, 1990). Reasons for variability in the association between parental alcohol involvement and adolescent AEs include sample heterogeneity and use of different measures of alcohol involvement and expectancies (e.g., Sher et al., 1991). Another important difference relates to the time lag between longitudinal assessments. Collins and colleagues (Collins & Graham, 2002) noted that the association between two variables can change dramatically across different measurement intervals and highlighted the importance of temporal design—defined as “the timing, frequency, and spacing of observations in a longitudinal study” (Collins, 2006, p.508)—for longitudinal studies of developmental processes (see Handley & Chassin, 2009). Greater attention to temporal design will clarify the status of mediational hypotheses about parental alcohol involvement and adolescent AEs (also see Sher et al., 1996).
Adolescent AEs and Parental Alcohol Involvement as Independent Risk Factors for Adolescent Alcohol Involvement
The current findings are most consistent with the hypothesis that parental alcohol involvement and AEs represent independent risk factors for subsequent alcohol involvement among adolescents (e.g., Mann, Chassin, & Sher, 1987). This pattern of results represents a conceptual replication of previous work showing unique longitudinal associations between parental alcohol involvement and AEs and subsequent alcohol involvement in adolescents (e.g., Reese et al., 1994). However, to our knowledge, ours is the first study to find unique longitudinal effects of maternal drinking and paternal AUD (assessed in middle childhood) and positive and negative AEs (assessed in early adolescence) on different dimensions of alcohol involvement (assessed in middle adolescence). Thus, at least in this sample, the evidence suggests that a) cognitive factors may be of greater importance than parental factors in terms of whether or not an adolescent decides to engage in risk drinking; and b) paternal AUD may be of greater importance than cognitive factors in terms of the frequency of engaging in risk drinking.
Limitations
The present study has several limitations. First, the sample was limited to adolescent boys, and there is some evidence that the effects of parental alcohol involvement and AEs may be different for adolescent girls (see Pastor & Evans, 2003). Second, the sample was limited to Caucasians, and evidence showed that ethnicity moderates some associations between expectancies and alcohol involvement (Chartier, Hesselbrock, & Hesselbrock, 2009). Third, we relied on adolescents' self-reports of alcohol use. Although self-report measures of alcohol involvement seem to have adequate reliability and validity (Babor, Steinberg, Anton, & Del Boca, 2000), there are numerous factors that influence the validity of self-reports, including age and forgetting (Brener, Billy, & Grady, 2003; Del Boca & Darkes, 2003), and these factors may have a stronger effect on self-report in younger adolescents. Fourth, the restricted range/class of families in the “low risk” group may have contributed to the nonsignificant correlations we observed between parental AUD and adolescent AEs. In addition, our use of 3-year assessment intervals, combined with the relatively small sample size, might have reduced the likelihood of detecting some of the hypothesized mediational processes. Further research using designs that combine shorter (e.g., daily) and longer time lags will clarify the status of the expectancy mediation hypothesis.
Conclusions and Implications
Despite these limitations, the present study has several important strengths. Results build on our earlier work showing that alcohol schemas form as early as age 3 ( Zucker et al., 1995), and our design allowed us to follow children and their parents from middle childhood to middle adolescence, which covers the critical period during which adolescents first begin experimenting with alcohol and other drugs (Zucker, 2006). Also, by tracking children who transitioned from nondrinkers in middle childhood to drinkers in middle adolescence, we were able to confirm that AEs precede the development of risky drinking. Intervention studies have shown that AEs are amenable to experimental manipulation, and challenges to expectancies predict reductions in alcohol consumption among males (e.g., Dunn, Lau, & Cruz, 2000). The present results highlight the potential utility of challenges targeting positive AEs for reducing risky drinking behaviors.
Our findings are particularly important in light of recent evidence that family influences on adolescent substance use are more pervasive than peer and neighborhood influences ( Ennett et al., 2008) and persist through late adolescence (Wood, Read, Mitchell, & Brand, 2004). An important avenue for further work is identification of how different risk and protective factors at different levels influence one another (Buu et al., 2009). Also, while expectancies are clearly important precursors of alcohol involvement, other cognitive constructs (e.g., substance use intentions; Anderson, Smith, & Fischer, 2003) and personality constructs (e.g., resilience; Lee & Cranford, 2008) should also be considered.
It is important to note that these results were obtained across a particularly crucial period in adolescent development. Parents and adolescents were assessed when offspring were in middle childhood (ages 9–11, M age = 10.6 years), early adolescence (ages 12–14, M age = 13.5 years), and mid-adolescence (ages 15–17, M age = 16.5 years). Evidence showed that positive AEs increase over this period ( Dunn & Goldman, 1998), particularly among adolescents exposed to peer and parental drinking (Cumsille, Sayer, & Graham, 2000). Yet, in a recent review, Windle et al. (2008, p. S285) asserted that “Unfortunately, we do not yet have longitudinal data mapping the progression of expectancy endorsement and its prediction of subsequent drinking among children 10 to 12 years of age. This gap in the literature is an important one that needs to be rectified.” Windle et al. noted that this period usually involves the transition to middle school and adolescence; as such, “this transition may become a turning point for some children, and their developmental trajectories may become characterized by maladaptive features.” The current study addresses this gap in the literature and demonstrates the unique longitudinal effects of maternal and paternal alcohol involvement and adolescent AEs for specific dimensions of underage drinking.
Footnotes 1 Brown et al. (1987) noted that, compared to the adult version of the AEQ, for the AEQ-A “statements are worded more generally to accommodate adolescents who have had little or no experience with alcohol” (p. 485). Specifically, “the Adult AEQ involves statements regarding the effects of alcohol on the respondent, whereas the Adolescent AEQ focuses on the effect of alcohol on people in general” (p. 488). For example, the adult AEQ item “Drinking makes me feel good” was modified for the AEQ-A to “Drinking alcohol makes a person feel good and happy.” We agree with Brown et al. that such modifications likely make the AEQ-A items more applicable to the entire adolescent population, including “adolescents who have not yet had direct or personal experience with alcohol” (p. 489). Because the MLS by design recruited a sample of families that was likely to have extensive experience with alcohol, we decided to retain the original wording of the items such that they referred to the adolescent. Recognizing that even in a high-risk sample not all adolescents will have consumed alcohol, each item was framed as a pure expectancy, rather than as a putative effect of alcohol involvement. Returning to the earlier example, the AEQ item “Drinking makes me feel good” was modified for the AEQ-A to “Drinking alcohol makes a person feel good and happy,” and we in turn modified this item for the BOQ to “Drinking alcohol would make me feel good.”
2 Three cases were identified as outliers (i.e., more than 3 standard deviations above the mean; Stevens, 1998) on this variable. All analyses were conducted with and without these three outlier cases. The results did not differ for the two sets of analyses. Because descriptive statistics were unduly influenced by these outlier cases, all descriptive statistics are reported with the outlier cases excluded.
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Submitted: September 21, 2009 Revised: March 26, 2010 Accepted: April 5, 2010
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Source: Psychology of Addictive Behaviors. Vol. 24. (3), Sep, 2010 pp. 386-396)
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Record: 39- Person-environment transactions in youth drinking and driving. Pedersen, Sarah L.; McCarthy, Denis M.; Psychology of Addictive Behaviors, Vol 22(3), Sep, 2008 pp. 340-348. Publisher: American Psychological Association; [Journal Article] Abstract: Drinking and driving is a significant health risk behavior for adolescents. This study tested mechanisms by which disinhibited personality traits (impulsivity and sensation seeking) and aspects of the adolescent home/social environment (parental monitoring and alcohol accessibility) can influence changes in drinking and driving behavior over time. Two hundred two high school age youths were assessed at 2 time points, approximately 8 months apart. Zero-inflated Poisson regression analyses were used to test (a) an additive model, where personality and environmental variables uniquely predict drinking and driving engagement and frequency; (b) a mediation model, where Time 2 environmental variables mediate the influence of disinhibited personality; and (c) an interaction model, where environmental factors either facilitate or constrain the influence of disinhibited personality on drinking and driving. Results supported both the additive and interaction model but not the mediation model. Differences emerged between results for personal drinking and driving and riding with a drinking driver. Improving our understanding of how malleable environmental variables can affect the influence of disinhibited personality traits on drinking and driving behaviors can help target and improve prevention/intervention efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Person–Environment Transactions in Youth Drinking and Driving
By: Sarah L. Pedersen
Department of Psychological Sciences, University of Missouri—Columbia
Denis M. McCarthy
Department of Psychological Sciences, University of Missouri—Columbia;
Acknowledgement: This research was supported by National Institute on Alcohol Abuse and Alcoholism Grants R03 AA13399 (Denis M. McCarthy, principal investigator) and T32 AA13526.
Youth drinking and driving is a significant public health problem. Motor vehicle accidents are the most common cause of death for high school age youths in the United States (Centers for Disease Control and Prevention, 2004), and statistics for 2005 indicate that 23% of drivers ages 15–20 killed in motor vehicle crashes had a blood alcohol concentration (BAC) at or above .08 (National Highway Traffic Safety Administration, 2006). Recently, O'Malley and Johnston (2007) found that 14.2% of high school seniors report engaging in drinking and driving behavior in the past 2 weeks, and 20.9% report riding with a drinking driver. Although young people are less likely to report driving after alcohol use than older drivers are (Royal, 2003), they consume a greater amount of alcohol before driving and consider it safe to drive at higher BAC levels than do older drivers (Hingson & Winter, 2003). This is of particular concern because the relative risk of fatal car accidents is higher for young drivers at all BAC levels, and risk increases faster for youths as BAC increases (Zador, Krawchuk, & Voas, 2000).
Changes in alcohol control policy, such as increasing the minimum drinking age and lowering BAC limits, have led to significant reductions in youth drinking and driving (O'Malley & Johnston, 2003; Wagenaar, O'Malley, & LaFond, 2001). The effectiveness of these policies makes clear the impact of environmental contingencies on youth drinking and driving decisions. On the other hand, there is evidence that drinking and driving prevalence has become relatively stable (O'Malley & Johnston, 2003, 2007; Sweedler et al., 2004). Drinking and driving also has a high rate of recidivism (Nochajski & Stasiewicz, 2006). The stability of this behavior highlights the potential role of individual difference characteristics that can put youths at risk for drinking and driving.
The present study tested an integrated model of personality and environmental influences on youth drinking and driving. Personality and developmental psychology theory (Buss, 1987; Caspi & Roberts, 2001; Scarr & McCartney, 1983) has emphasized the importance of mechanisms by which heritable individual difference characteristics, such as personality traits, can influence or interact with environmental/contextual factors across the lifespan. Although often referred to as Gene × Environment interactions, following Caspi and Roberts (2001), we use the term person–environment transactions to describe these processes, as this term is neutral regarding the genetic basis of the characteristics under study, as well as the statistical/analytic model of how person characteristics and environments are associated. The present study tested person–environment transactions between disinhibited personality traits (sensation seeking, impulsivity) and aspects of the adolescent home/social environment (parental monitoring, alcohol accessibility) in determining drinking and driving behavior.
Personality Characteristics: Sensation Seeking and ImpulsivityA number of personality characteristics are associated with substance use and risk-taking behaviors in adolescence (Caspi et al., 1997; Elkins, King, McGue, & Iacono, 2006). The personality domain of impulsivity/disinhibition has been found to have the strongest and most consistent relationship with alcohol-related and antisocial behaviors (Sher & Trull, 1994). Recent conceptual work has argued that two of the most studied facets of this domain, impulsivity and sensation seeking, should be considered distinct constructs (Whiteside & Lynam, 2001) that are only moderately correlated (Zuckerman, 1994). Impulsivity can be defined as the tendency to experience and act on strong impulses (Whiteside & Lynam, 2001), while sensation seeking can be defined as desiring new and intense experiences (Zuckerman & Kuhlman, 2000). Impulsivity and sensation seeking have been shown to predict different externalizing behaviors and psychiatric diagnoses (Fischer, Smith, & Anderson, 2003; Lynam & Miller, 2004; Miller, Flory, Lynam, & Leukefeld, 2003; Smith et al., 2007; Whiteside, Lynam, Miller, & Reynolds, 2005). Even when these traits predict similar risk-taking behaviors, it has been argued (Whiteside & Lynam, 2001) that they may do so for different reasons. For example, individuals high in sensation seeking may engage in risk taking as a means of experiencing excitement or thrills, while an impulsive individual may engage in the same behavior in response to strong affect.
There is considerable evidence for both sensation seeking and impulsivity as predictors of alcohol-related behaviors (Hittner & Swickert, 2006; White, Bates, & Buyske, 2001). A literature review of sensation seeking and risky driving behavior (Jonah, 1997) found that most studies reported significant relations between sensation seeking and drinking and driving behavior. Results were consistent between studies of adults and adolescents. High impulsivity has been associated with drinking and driving, riding with a drinking driver, and binge drinking (Ryb, Dischinger, Kufera, & Read, 2006). Impulsivity is also correlated with drinking and driving violations in adult men (Eensoo, Paaver, Harro, & Harro, 2005).
Relatively little is known about specific mechanisms by which personality characteristics might influence adolescent drinking and driving behavior. In adults, Stacy and colleagues conducted both cross-sectional (Stacy, Newcomb, & Bentler, 1991) and prospective (Stacy & Newcomb, 1998) studies that found that disinhibited personality traits influence drinking and driving through alcohol use behavior. Turrisi, Jaccard, and McDonnell (1997) found that the influence of emotional control, a combination of impulsivity and sensation seeking, on drinking and driving was mediated by cognitions about drinking and driving and drinking and driving alternatives. To our knowledge the current study is the first to test potential mechanisms of personality risk for drinking and driving involving two aspects of the adolescent home/social environment: parental monitoring and alcohol accessibility.
Parental Monitoring and Alcohol AccessibilityParenting characteristics are thought to play a significant role in the development of problem behavior in youths. In particular, low levels of parental monitoring are associated with increased risk for a variety of adolescent risk-taking behaviors, including unsafe sexual activity and drug use (Li, Stanton, & Feigelman, 2000) as well as stealing, fighting, and destroying property (Curran & Chassin, 1996). Youth report of parents' knowledge of their behavior is associated with their alcohol use (Chassin, Pillow, Curran, Molina, & Barrera, 1993; Curran & Chassin, 1996) and frequency of heavy episodic drinking (Wood, Read, Mitchell, & Brand, 2004). Longitudinal studies of drinking and driving behavior have shown that low parental monitoring in high school prospectively predicted increased likelihood of drinking and driving (Bingham & Shope, 2004) and increased rate of serious driving offenses (Shope, Waller, Raghunathan, & Patil, 2001).
The accessibility of alcohol in adolescents' social/community environment has also been shown to have considerable impact on their alcohol-related behavior. Self-reported ability to obtain alcohol has been found to be related to alcohol consumption in adolescence (Jones-Webb et al., 1997). At the community level, studies have shown that the number of registered alcohol vendors (Treno, Grube, & Martin, 2003), reported use of alcohol vendors, and perceived community enforcement of underage drinking laws (Dent, Grube, & Biglan, 2005) are related to youth drinking and drinking and driving behavior.
Integrating Environmental and Personality RiskThe present study tested person–environment transactions in the development of youth drinking and driving behaviors. We hypothesized that disinhibited personality traits not only exert a direct influence on drinking and driving behavior but can be mediated by or interact with other important risk factors, such as parenting and social/contextual factors.
A sample of high school age youths was assessed at two time points, approximately 8 months apart. We first tested an additive model, where both personality (impulsivity and sensation seeking) and environmental (parental monitoring and alcohol accessibility) factors were hypothesized to make unique contributions to the prediction of drinking and driving and riding with a drinking driver, controlling for prior alcohol use, license status, gender, and drinking and driving behavior. Models were tested separately for drinking and driving and riding with a drinking driver, as prior studies have indicated that these are distinct behaviors that may have different risk mechanisms (McCarthy & Brown, 2004; Poulin, Boudreau, & Asbridge, 2007; Yu & Shacket, 1999).
We then tested a mediation model, examining potential indirect effects of personality on drinking and driving behavior through their influence on parental monitoring and alcohol accessibility. This model reflects the hypothesis that disinhibited personality traits can influence the response of others in youths' environment, as well as the environments that youths select. For example, disinhibited youths may be more difficult for parents to monitor and less likely to disclose information to parents. Disinhibited youths might also be more likely to select environmental contexts such as a deviant or substance-using peer group, which allow for easier access to alcohol. Although this hypothesis has not been tested directly elsewhere, recent longitudinal studies have demonstrated that youth delinquent behavior can alter parental monitoring/knowledge over time (Laird, Petitt, Bates, & Dodge, 2003) and that personality traits can influence deviant peer affiliation (Yanovitzky, 2005).
Finally, we tested an interaction model, examining whether the association of personality factors with drinking and driving behavior is moderated by parental monitoring and/or alcohol accessibility. There is some evidence that aspects of the home/social environment can constrain or exacerbate substance-related behavior in adolescents. For example, parental involvement has been found to moderate the influence of other family factors on child internalizing problems (Burstein, Stanger, Kamon, & Dumenci, 2006), while maternal support and discipline interact with peer substance use in the development of adolescent substance use over time (Marshal & Chassin, 2000). We hypothesized that high levels of parental monitoring would constrain drinking and driving behavior, such that youths high in sensation seeking or impulsivity would be less likely to drink and drive when parental monitoring is high. For alcohol accessibility, we hypothesized that impulsive or sensation seeking youths would be more likely to drink and drive when alcohol is easily obtained in their environment.
Method Participants
Study participants were 266 high-school-age youths. Of the original sample, 202 (76%) completed the Time 2 survey approximately 8 months later. Participants who did not complete the second survey did not differ from those who did in age, gender, ethnicity, license status (Time 1), or drinking and driving behavior (Time 1). Attriters were more likely to report drinking behavior at Time 1 (77% vs. 60%), χ2(1, N = 266) = 5.85, p < .05, and were more likely to be African American (57% vs. 19%), χ2(1, N = 266) = 24.14, p < .01.
The final sample of 202 participants was primarily Caucasian (85%), with 7% African American and 8% of other racial backgrounds. The sample was 66% female and had a mean age of 16.15 years (SD = 1.00, range 13–18) at Time 1. During the first assessment, 45% of the sample were nondrivers. At Time 2, 20% were nondrivers, 25% were recently licensed drivers, and the remaining 55% were established drivers, driving independently at both time points of the study.
Procedures
Participants were recruited from local high schools through flyers passed out during lunch breaks and after school. Study flyers were also posted in locations frequented by youths (stores, theaters, etc.) throughout the community. Interested participants contacted the research lab and were given more information about the study. For participants under age 18, verbal parental consent was obtained. Participants were then mailed a packet with questionnaires, consent forms, assent form (if under age 18), postage-paid return envelope, and a cover letter. Upon returning completed study materials, participants were each mailed a $20 gift certificate to the local mall. Participants were contacted approximately 7 months later and asked if they would like to participate in a follow-up study. Procedures were otherwise identical to those for Time 1. Participants were again compensated with a $20 gift certificate to the local mall upon completion of the materials. Study procedures were approved by the University of Missouri—Columbia Institutional Review Board.
Measures
Demographic information
A self-report questionnaire was used to collect demographic information, including age, gender, and ethnicity.
Alcohol use
The Drinking Styles Questionnaire (DSQ; Smith, McCarthy, & Goldman, 1995) was used to assess alcohol use at Time 1 and Time 2. The DSQ collects information about drinking status, quantity and frequency of drinking, frequency of drinking to intoxication, and typical drinking situations. Typical frequency of alcohol consumption at Time 1 and past month frequency of alcohol use at Time 2 were used as covariates in the present study. The DSQ has demonstrated good reliability and validity in adolescent and college-age samples (McCarthy, Miller, Smith, & Smith, 2001; Smith et al., 1995).
Drinking and driving behaviors
Participants were asked to report frequency of driving after consuming any alcohol and riding with a driver who had consumed alcohol. Participants retrospectively reported on drinking and driving behaviors over the past 3 months at both time points.
Personality characteristics
Sensation seeking and impulsivity were measured at Time 1 with the Zuckerman–Kuhlman Personality Questionnaire (ZKPQ: Zuckerman, Kuhlman, Joireman, Teta, & Kraft, 1993). The ZKPQ is a 38-item self-report measure with a dichotomous response format. The 19 items that comprise the Impulsivity and Sensation Seeking subscales were included in the present study (8 items on impulsivity, 11 items on sensation seeking). The mean of each subscale (range 1–2) was calculated for each participant, with higher scores representing higher levels of impulsivity and sensation seeking. Internal consistency reliabilities for the impulsivity (α = .63) and sensation seeking (α = .71) subscales in this sample were adequate.
Alcohol accessibility
Alcohol accessibility was assessed at Time 2. Three questions were adapted from a previous study (Smart, Adlaf, & Walsh, 1996) of perceived access to and procurement of alcohol by youths. Two questions asked youths to rate the likelihood that they would be able to obtain alcohol themselves if they wanted. A 6-point Likert-type scale was used, with responses ranging from no chance to certain to happen. Youths were also asked, on a 6-point scale ranging from never to 20 or more times, how often they had obtained alcohol in the past year. The scale mean (range 1–6) was calculated for each participant, with higher scores representing easier accessibility of alcohol. Internal consistency reliability for these items in this sample was adequate (α = .74).
Parental monitoring
Parental monitoring was assessed at Time 2. A six-item measure used in previous studies (Li et al., 2000) asked youths to rate their parents' knowledge of their activities (e.g., “my parents know where I am after school”). A 5-point Likert-type scale was used, with responses ranging from disagree strongly to agree strongly. The scale mean (range 1–5) was calculated for each participant with reverse-coded items so that higher scores represented lower levels of parental monitoring. This measure has been found to be internally consistent and valid in studies of adolescents (Li et al., 2000). Internal consistency reliability for these items in this sample was very good (α = .87).
Analytic Strategy
We tested the study hypotheses using zero-inflated Poisson (ZIP) regression analyses in Mplus 4.2 (Muthén & Muthén, 1998–2007). Poisson regression is appropriate when the dependent variable is a count of the number of events over a fixed period of time, such as the number of times engaging in drinking and driving behavior during a given time span. The ZIP model includes a correction for overdispersion that occurs when the most common frequency count is zero. Mplus estimates two components in a ZIP model. The first, a zero-inflation component, is similar to logistic regression and estimates the odds of being in the zero class, or not reporting engagement in the specified behavior (e.g., the odds of not drinking and driving). The second component is a Poisson regression analysis, which estimates the predicted rate (PR) of engaging in that behavior if the individual is able to assume a nonzero status (e.g., the frequency of drinking and driving among those who drink and drive).
To simplify reporting, we inverted odds ratios (ORs) from the logistic regression component so that higher values indicated greater likelihood of being in the nonzero class, or engaging in drinking and driving behavior. For the Poisson regression component, Poisson regression coefficients were used to calculate a PR value, which indicates the expected rate of increase in the dependent variable under different combinations of the independent variables (Cohen, Cohen, West, & Aiken, 2003). To ease interpretation of Poisson results, we standardized personality and environmental variables as z scores. Models predicting drinking and driving behavior included only participants who were licensed drivers at Time 2, while models for riding with a drinking driver included all study participants. To control for differences in license status across analyses, we created dummy-coded variables of license groups for drinking and driving models (newly licensed/established drivers) and models predicting riding with a drinking driver (never licensed/licensed, new or nonlicensed/established drivers).
Results Descriptive Statistics
Table 1 presents mean levels of sensation seeking, impulsivity, alcohol accessibility, parental monitoring, percent reporting lifetime alcohol use, percent reporting drinking and driving and riding with a drinking driver, and frequency of drinking and driving behaviors for those who engaged in the behavior. No significant gender differences were found for engagement in drinking and driving behaviors, alcohol use, personality characteristics, or environmental factors at either time point.
Descriptive Statistics for Personality and Environmental Variables, Lifetime Alcohol Use, and Drinking and Driving Behaviors
Additive Risk Model of Drinking and Driving Behaviors
We first tested whether impulsivity (Time 1), sensation seeking (Time 1), alcohol accessibility (Time 2), and parental monitoring (Time 2) uniquely predicted frequency of drinking and driving or riding with a drinking driver at Time 2 over and above Time 1 drinking and driving behaviors, license status, frequency of alcohol use (Time 1), and gender. Results for the logistic regression portion of the model indicated that when all study variables were included in the model, only Time 1 alcohol use frequency (OR = 1.63, p < .05) uniquely predicted Time 2 engagement in drinking and driving. Time 1 alcohol use frequency (OR = 1.74, p < .01), frequency of riding with a drinking driver (OR = 1.16, p < .05), and sensation seeking (OR = 2.12, p < .01) predicted Time 2 engagement in riding with a drinking driver. License status variables were not related to engagement for either drinking and driving or riding with a drinking driver.
Results for the Poisson regression portion of the model are presented in Table 2. Time 1 alcohol use frequency, gender, and sensation seeking predicted frequency of both drinking and driving and riding with a drinking driver at Time 2. Frequency of riding with a drinking driver at Time 1 predicted frequency of this behavior at Time 2. Impulsivity predicted frequency of drinking and driving but not riding with a drinking driver. Parental monitoring and alcohol accessibility at Time 2 predicted frequency of riding with a drinking driver but not drinking and driving.
Additive Model Predicting Time 2 Frequency of Drinking and Driving Behaviors
To test whether the influence of personality and environmental variables on drinking and driving behaviors was accounted for by concurrent alcohol use, we also ran analyses including frequency of past month drinking at Time 2. For the logistic regression portion of the model, Time 2 alcohol use frequency was not significantly associated with engagement in either behavior. As a result, the pattern of significant results for this model remained unchanged. For the Poisson regression portion of the model, Time 2 alcohol use frequency was associated with frequency of both drinking and driving (PR = 0.05, p < .001) and riding with a drinking driver (PR = 0.97, p < .001). The inclusion of Time 2 alcohol use frequency did not change the pattern of results for riding with a drinking driver: Sensation seeking (PR = 0.60, p < .001), alcohol accessibility (PR = 0.99, p < .001), and parental monitoring (PR = 1.10, p < .001) remained significantly associated with frequency of this behavior. However, for drinking and driving, sensation seeking (PR = 0.05, p < .01) was associated with frequency over and above Time 2 alcohol use frequency, while impulsivity was no longer related (PR = 0.04, ns).
Indirect Effects of Personality on Drinking and Driving Behaviors
We then examined whether the prediction of engagement and frequency of drinking and driving behaviors by disinhibited personality traits was mediated by parental monitoring or alcohol accessibility. Several conditions must be present for mediation to be indicated (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). One condition is that the independent and mediator variables must be associated. Correlation analyses indicated that Time 1 sensation seeking was moderately associated with Time 2 alcohol accessibility (r = .21, p < .01) and parental monitoring (r = .18, p < .05), while impulsivity was not. Another requirement for mediation is that the mediator and dependent variable be associated. Results from the additive model indicated that neither alcohol accessibility nor parental monitoring was related to engagement in either behavior. For the Poisson regression portion of the model, both alcohol accessibility and parental monitoring predicted frequency of riding with a drinking driver, but neither predicted drinking and driving. These results indicate that the influence of sensation seeking on frequency of riding with a drinking driver could be mediated by alcohol accessibility or parental monitoring.
A final condition for mediation is that the association between the independent and dependent variables either drop significantly or reduce to zero when the mediator is included in analyses. Results of the additive model do not support full mediation of sensation seeking's influence on riding with a drinking driver. To test potential partial mediation, we compared the Poisson regression coefficients for sensation seeking predicting riding with a drinking driver when either parental monitoring or alcohol accessibility was included in analyses. Mediation analyses controlled for license status, alcohol use frequency at Time 1, gender, and Time 1 drinking and driving behavior. Results indicated that these coefficients did not differ when either parental monitoring or alcohol accessibility was included in the model. Results therefore do not support mediation of sensation seeking's association with riding with a drinking driver.
Personality × Environment Interactions
Finally, we tested potential interactions between disinhibited personality traits and alcohol accessibility or parental monitoring in the prediction of drinking and driving behaviors. We estimated separate ZIP models for each of four potential interactions (Sensation Seeking or Impulsivity × Alcohol Accessibility or Parental Monitoring). Centered variables were used to create product terms for each potential interaction. For each model, study covariates, the relevant personality and environmental variables, and the corresponding product term were entered as predictors. Results indicated several significant interactions in the prediction of drinking and driving behavior and one interaction in the prediction of riding with a drinking driver.
Impulsivity interacted with both parental monitoring (p < .05) and alcohol accessibility (p < .05) in predicting Time 2 drinking and driving frequency. These interactions were probed by estimating models at 1 SD above and below the mean on parental monitoring and alcohol accessibility (see Figure 1). For ease of interpretability, analyses for probing and graphing interactions did not include study covariates. For youths who reported high alcohol accessibility, increases in impulsivity were associated with greater increases in drinking and driving frequency (PR = 6.56) than for those who reported low alcohol accessibility (PR = 3.64). For youths who reported low parental monitoring, increases in impulsivity were associated with greater increases in drinking and driving frequency (PR = 6.55) than for those reporting high parental monitoring (PR = 4.35).
Figure 1. Graphs represent interactions of impulsivity with parental monitoring and alcohol accessibility from Poisson regression analyses for frequency of drinking and driving. Lines depict predicted rate differences at 1 SD above and below the mean for parental monitoring and alcohol accessibility. PM = parental monitoring; AA = alcohol accessibility. ps < .05.
Results also indicated that sensation seeking interacted with alcohol accessibility in predicting Time 2 drinking and driving in both the logistic regression (p < .05) and Poisson regression (p < .001) components. Additionally, sensation seeking interacted with parental monitoring to predict Time 2 engagement in riding with a drinking driver (p < .05). Probing these interactions indicated that increases in sensation seeking were associated with a greater frequency of drinking and driving for youths reporting high alcohol accessibility (OR = 1.24; PR = 6.54) than for youths reporting low alcohol accessibility (OR = 0.52; PR = 4.23; see Figure 2). Higher sensation seeking was also associated with increased likelihood of riding with a drinking driver for youths reporting low parental monitoring (OR = 1.32) compared to youths reporting high parental monitoring (OR = 0.72).
Figure 2. Graph represents the interaction of sensation seeking with alcohol accessibility from Poisson regression analyses for frequency of drinking and driving. Lines depict predicted rate differences at 1 SD above and below the mean for alcohol accessibility. p < .001.
DiscussionThe goal of the current study was to test potential mechanisms by which personality traits and environmental risk factors might influence adolescents' drinking and driving behaviors. Our results provide support for an additive model, where both personality and environmental factors make unique contributions to drinking and driving behaviors over time. In the additive model, high sensation seeking youths reported increased frequency of both personal driving after drinking and riding with a drinking driver. Importantly, results were significant while controlling for license status, frequency of alcohol use at both time points, and Time 1 drinking and driving behaviors. Although impulsivity predicted frequency of drinking and driving in the additive model, it did not uniquely predict drinking and driving over concurrent alcohol use. This is consistent with prior studies in adults (Stacy & Newcomb, 1998; Stacy et al., 1991) and may indicate that the influence of impulsivity on drinking and driving is mediated by its association with drinking behavior.
There was also evidence for interaction effects, such that disinhibited personality traits led to more frequent drinking and driving in youths for whom alcohol is easily accessible or who reported low parental monitoring of their behavior. Results did not support mediation of risk from disinhibited personality traits by the aspects of adolescents' home/social environment that were assessed in this study.
Several differences emerged between models predicting drinking and driving and riding with a drinking driver. Parental monitoring and alcohol accessibility were related to only the frequency of accepting a ride from a drinking driver and not personal drinking and driving behavior. Also, Time 1 alcohol use frequency predicted later drinking and driving but was not related to riding with a drinking driver. As noted, prior studies have found evidence for distinct risk mechanisms for these two behaviors. For example, adolescents are less likely to ride with a drinking driver when they have a driver's license (McCarthy & Brown, 2004; Poulin et al., 2007). Results of the present study provide evidence that personal drinking and driving decisions are more strongly influenced by individual difference characteristics, such as desiring intense or stimulating experiences. Riding with a drinking driver appears to be more strongly influenced by external factors such as parental monitoring, and these differences remained even after controlling for the effect of license status. However, there was also evidence for moderation of the influence of disinhibited personality traits on personal drinking and driving. The accessibility of alcohol and degree of parental monitoring either facilitated or constrained drinking and driving risk for disinhibited youths.
Personality characteristics, parental monitoring, and alcohol accessibility did not predict increased likelihood of engagement in drinking and driving behaviors once prior drinking and driving behaviors and alcohol use were accounted for. The exception was sensation seeking, which predicted engagement in riding with a drinking driver. These results may be due, in part, to the relatively brief time period of the study (approximately 8 months), which limited the number of youths who initiated drinking and driving over the course of the study. These findings may also be the result of strong bivariate associations of Time 1 alcohol use frequency (OR = 2.20, p < .001) and drinking and driving (OR = 1.85, p < .001) with engagement in drinking and driving at Time 2, making it difficult for study variables to add unique prediction.
We also did not find support for mediation of personality risk for drinking and driving by environmental factors. Impulsive personality traits do not appear to influence either parental monitoring or alcohol accessibility. Although sensation seeking youths reported lower parental monitoring and greater access to alcohol at Time 2, this did not explain sensation seeking's influence on drinking and driving behaviors. However, these results provide some evidence that sensation seeking might influence changes in these environmental characteristics over time. While the magnitude of sensation seeking's bivariate association with parental monitoring and alcohol accessibility was modest, it is similar to that observed between five-factor personality traits and parent and peer support (Asendorpf & van Aken, 2003) in longitudinal studies supporting transactional associations over time. One direction for future research is to examine whether sensation seeking is associated with changes in alcohol accessibility and parental monitoring over a longer developmental period. Having additional time points would also allow for a more stringent test of how personality may influence environmental factors and subsequent drinking and driving behaviors over time.
Another direction for future research is to examine other environmental and contextual factors that might mediate the influence of disinhibited personality traits on drinking and driving. For example, sensation seeking has been found to influence alcohol and drug use in part through influencing youths' associations with deviant or substance-using peers (Yanovitzky, 2005). Associating with deviant peers may help explain why sensation seeking was related to riding with a drinking driver in the current study. There is also evidence that drinking context (e.g., outdoors, at bars; Usdan, Moore, Schumacher, & Talbott, 2005) and lack of transportation planning prior to drinking (Nelson, Kennedy, Isaac, & Graham, 1998) are associated with drinking and driving behaviors. Further research is required to test whether disinhibited youths are more likely to drink in situations where drinking and driving is likely to occur or whether they are less likely to plan for transportation prior to drinking.
The current study also showed changes in the frequency and engagement of drinking and driving over time (see Table 1). One potential reason for this pattern of results is that youths who just begin drinking and driving over the course of the study may initially engage in this behavior less frequently than do those individuals with more established drinking and driving behaviors. Data from the current study tentatively support this possibility. Youths who did not report drinking and driving at Time 1 but did at Time 2 reported an average of 2.8 drinking and driving occasions. Individuals who reported drinking and driving at both time points reported an average of 6.0 drinking and driving occasions at Time 2. Future studies could more directly explore how the rate of increase of drinking and driving frequency changes over time.
There are several limitations to the generalizability of the current study. Participants were primarily recruited from high school campuses in the central Missouri area. There are significant regional differences in the prevalence of drinking and driving behavior, with higher rates in the Midwest (Chou et al., 2006). In addition, although efforts were made to recruit high school age youths from community sources, the use of school-based recruitment can introduce sample biases due to absenteeism, truancy, or disengagement from academics by some youths, particularly disinhibited or substance-involved youths. Female adolescents were also overrepresented in our sample. Although there is ample evidence that male adolescents are at greater risk for engaging in and experiencing consequences of drinking and driving (Hingson & Winter, 2003; Wechsler, Lee, Nelson, & Lee, 2003), rates of drinking and driving behaviors did not differ by gender. Although gender was controlled for in study analyses, our sample size prevented us from conducting study analyses separately by gender.
The study relied on self-report for assessing all study variables. Studies have demonstrated that self-report measures of alcohol use and related behavior can be valid in youths, particularly when data collection is confidential or anonymous and when no consequences are associated with the report (Smith et al., 1995; Wilson & Grube, 1994). For parental monitoring and alcohol accessibility, youth report may not provide an accurate representation of these two constructs. However, there is some evidence that youths' perceptions of the home environment, such as parental behaviors, are most relevant in determining youth behavior (Smith, Miller, Kroll, Simmons, & Gallen, 1999). Nevertheless, studies of both parental monitoring (Laird et al., 2003) and alcohol accessibility (Dent et al., 2005) have demonstrated that supplementing youth report with parent report or community level information can provide a fuller assessment of these constructs. Additionally, parental monitoring and alcohol accessibility were assessed only at Time 2, which limited our ability to test how changes in these environmental factors influence drinking and driving behaviors over time.
Recent research on parental monitoring has also indicated greater complexity of this construct than is reflected in this study. Studies have found differences in the influence of what parents know (parental knowledge) and how they know it (active efforts to monitor behavior, child disclosure) on youth behavior (Kerr & Stattin, 2000). In studies of youth substance use and delinquency, youth self-disclosure and parental knowledge have been found to mediate the influence of parenting style on youth behavior (Soenens, Vansteenkiste, Luyckx, & Goossens, 2006), although there is also evidence for direct effects of parental control and monitoring (Fletcher, Steinberg, & Williams-Wheeler, 2004). It is important for future studies to test whether disinhibited personality traits have distinct influences on youth self-disclosure and parental knowledge.
Results of this study provide evidence for person–environment transactions in the development of youth drinking and driving behavior. It is well known that disinhibited personality traits predict drinking and driving and other risk-taking behaviors. Results of this study suggest that the disinhibited traits of impulsivity and sensation seeking may have different implications for driving after drinking or riding with a drinking driver. This is consistent with prior research demonstrating differential prediction of externalizing behaviors by these traits (e.g., Lynam & Miller, 2004; Miller et al., 2003). Improving our understanding of mechanisms by which these personality traits lead to specific risk-taking behaviors can improve prevention/intervention efforts. For example, alcohol control policies (Babor et al., 2003) and parental intervention strategies that increase communication and parental awareness (Turrisi, Jaccard, Taki, Dunnam, & Grimes, 2001) have been found to be effective in reducing youth alcohol use. Our results suggest that these may also be effective adolescent drinking and driving interventions, particularly when targeting disinhibited youths. As study results suggest that parental monitoring may be of particular importance for riding with a drinking driver, interventions that increase parental awareness and communication may also benefit from targeting youths prior to their obtaining a driver's license.
Footnotes 1 For each mediation test, one of the component paths is assessed as a standard regression/correlation coefficient (e.g., sensation seeking and alcohol accessibility), while the other is assessed as a Poisson regression coefficient (e.g., alcohol accessibility and drinking and driving). This lack of correspondence made several of the standard methods of testing mediation (product of coefficients, estimation of indirect effects) inappropriate in the current study. Instead, Poisson regression coefficients were compared with and without the mediator included in the model. In each case, the 95% confidence interval of the two coefficients overlapped considerably. Although this method is not ideal, for the present study we believe it was sufficient to demonstrate absence of mediation.
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Submitted: April 9, 2007 Revised: February 4, 2008 Accepted: February 6, 2008
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Source: Psychology of Addictive Behaviors. Vol. 22. (3), Sep, 2008 pp. 340-348)
Accession Number: 2008-11981-003
Digital Object Identifier: 10.1037/0893-164X.22.3.340
Record: 40- Predictors and outcomes of drinkers’ use of protective behavioral strategies. Jongenelis, Michelle I.; Pettigrew, Simone; Pratt, Iain S.; Chikritzhs, Tanya; Slevin, Terry; Liang, Wenbin; Psychology of Addictive Behaviors, Vol 30(6), Sep, 2016 pp. 639-647. Publisher: American Psychological Association; [Journal Article] Abstract: While protective behavioral strategies (PBSs) have the potential to reduce alcohol-related harm, there is a lack of understanding of the factors influencing adults’ use of these strategies. The present study assessed the frequency of enactment of a range of PBSs among Australian adults and identified factors associated with their use and the implications for alcohol harm minimization. A sample of 2,168 Australian drinkers (1,095 males and 1,073 females) recruited via a web panel provider completed an online survey that included items relating to quantity and frequency of alcohol consumption, beliefs about the health consequences of alcohol consumption, use of 5 specific PBSs (e.g., counting drinks and eating while drinking), and demographic characteristics. In general, use of these PBSs was negatively associated with overall alcohol consumption. However, usage rates were relatively low, especially among the heaviest drinkers. Refusing unwanted drinks and alternating between alcoholic and nonalcoholic beverages were identified as especially important strategies in the Australian context, accounting for a substantial proportion of the variance in alcohol consumption. Greater efforts to increase awareness and use of PBSs are warranted. In particular, the results suggest that information relating to the importance of refusing unwanted drinks and alternating between alcoholic and nonalcoholic beverages should be actively disseminated to the drinking public. In addition, the reliance on specified numbers of standard drinks in national drinking guidelines suggests encouraging drinkers to count their drinks should be a further focus of interventions given low reported prevalence of this behavior. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Predictors
and Outcomes of Drinkers’ Use of Protective Behavioral Strategies
By: Michelle I. Jongenelis
School of Psychology and Speech Pathology, Curtin University;
Simone Pettigrew
School of Psychology and Speech Pathology, Curtin University
Iain S. Pratt
Cancer Council Western Australia, Subiaco, Western Australia and School of Psychology and Speech Pathology, Curtin University
Tanya Chikritzhs
Faculty of Health Sciences, National Drug Research Institute, Curtin University
Terry Slevin
Cancer Council Western Australia, Subiaco, Western Australia and School of Psychology and Speech Pathology, Curtin University
Wenbin Liang
Faculty of Health Sciences, National Drug Research Institute, Curtin University
Acknowledgement: This study was supported by the Western Australian Health Promotion Foundation (Healthway), research grant 20338.
Protective behavioral strategies (PBSs) are actions that can reduce alcohol-related harm by decreasing the total amount of alcohol consumed and the amounts consumed on single drinking occasions (Grazioli et al., 2015; Napper, Kenney, Lac, Lewis, & LaBrie, 2014; Pearson, 2013). They can be categorized as either limiting/stopping drinking (e.g., deciding on a limit prior to drinking), modifying the manner of drinking (e.g., eating while drinking), or serious harm reduction (e.g., arranging a designated driver; Martens, Ferrier, & Cimini, 2007; Treloar, Martens, & McCarthy, 2015). A lack of awareness of or adherence to PBSs may prevent drinkers from restricting their alcohol intake to low-risk levels (Kelly, Chan, & O’Flaherty, 2012; Palmer, Corbin, & Cronce, 2010).
Most previous research investigating the use of PBSs has focused on American college students and young adults (Arterberry, Smith, Martens, Cadigan, & Murphy, 2014; Barry & Goodson, 2011; Braitman, Henson, & Carey, 2015; DeMartini et al., 2013; Grazioli et al., 2015; Kelly et al., 2012; Kenney & LaBrie, 2013; Kenney, Napper, LaBrie, & Martens, 2014; Linden-Carmichael, Braitman, & Henson, 2015; Linden, Lau-Barraco, & Milletich, 2014; Martens et al., 2007; Martens et al., 2004; Napper et al., 2014; Palmer et al., 2010; Pearson, D’Lima, & Kelley, 2013; Pearson, Kite, & Henson, 2013; Prince, Carey, & Maisto, 2013). These studies have typically found that PBSs reduce alcohol consumption and alcohol-related harms in these target groups and are more likely to be enacted by females. There is still much work to be done in this domain due to a general lack of research involving other drinker segments and the need for a more comprehensive understanding of the factors influencing PBS use (Lewis, Rees, & Lee, 2009), including relevant moderating and mediating relationships (Pearson, 2013; Pearson, Kite et al., 2013). For example, factors such as gender and attitudes to alcohol and its potential harms have been nominated as being potentially relevant to the decision to use PBSs (Pearson, 2013). Identifying the factors associated with use of PBSs at a population level and determining whether these vary for different PBSs can facilitate the development of appropriate interventions to advise drinkers of the nature and desirability of these strategies and overcome potential barriers to use.
A further important issue is whether different PBSs exhibit varying levels of effectiveness. To optimize their outcomes, future public education programs relating to PBSs need to focus on those strategies that have the greatest potential to reduce alcohol-related harms. Previous studies have tended to aggregate PBSs within categories or overall, preventing identification of relative effects of individual strategies (e.g., Arterberry et al., 2014; DeMartini et al., 2013; Napper et al., 2014; Treloar et al., 2015). Identifying the individual strategies that are most strongly associated with reduced alcohol intake can inform future efforts to minimize alcohol-related harms (Barry & Goodson, 2011).
The present study addresses these research gaps in the context of Australia, where average consumption levels are relatively high at approximately 10 L of pure alcohol per capita per year (World Health Organization, 2014) and alcohol-related harms are estimated to cost AUD$36 billion per annum (Foundation for Alcohol Research & Education, 2011) for a population of just 24 million (Australian Bureau of Statistics, 2016). The specific aims of the study were to (a) assess prevalence of engagement in protective behavioral strategies by alcohol consumption risk status and (b) identify factors associated with higher and lower levels of enactment of specific strategies and the associated implications for alcohol consumption.
MethodThe study was part of a broader examination of Australians’ alcohol-related beliefs and behaviors via a national online survey (Pettigrew et al., 2016). Ethics approval for the study was obtained from a University Human Research Ethics Committee.
Recruitment and Participants
A large web panel provider, PureProfile, provided access to a sample of 2,168 drinkers. The panel is comprised of 350,000 Australians who represent a broad range of geographic and socioeconomic groupings. Panel members are recruited via diverse strategies, including radio and Internet advertising, publicity, and referrals. Potential sample members for individual studies can opt in by either clicking through to the survey from a link provided to them in an invitation e-mail or by selecting the survey from a list of those made available to panel members on the PureProfile website. Panel members receive small financial incentives for participating in surveys and IP addresses are monitored to avoid multiple completions by the same individuals. PureProfile hosts the surveys, and no identifying information is contained in the data files provided to researchers.
Eligibility criteria for the present study were 18+ years of age and alcohol consumption at least twice per month. As a result, the sample was on average older and characterized by heavier drinkers compared to the periodic Australian national alcohol surveys conducted by the Australian Institute of Health and Welfare (AIHW, 2011, 2014) that use broader inclusion criteria (14+ years and any level of alcohol consumption (including nil) within the previous 12 months). Quotas were used to ensure equal proportions of males and females. The resulting sample profile is presented in Table 1.
Sample Profile (n = 2168)
Measures and Procedure
After providing informed consent, respondents completed an online survey that included items relating to quantity and frequency of alcohol consumption, beliefs about the health consequences of alcohol consumption, frequency of use of five PBSs, and demographic characteristics (age, gender, SES, education, income). Alcohol consumption was assessed as per the items used in national alcohol intake surveys (AIHW, 2011, 2014): “In the last 12 months, how often did you have an alcoholic drink of any kind?” and “On a day that you have an alcoholic drink, how many standard drinks do you usually have?” A figure sourced from the National Health and Medical Research Council (NHMRC, 2009) depicting standard drink quantities (with a standard drink containing 10 g of alcohol) across a broad range of beverage and container types was presented to respondents prior to the reporting of intake levels to facilitate accurate measurement.
Based on reported consumption, the NHMRC (2009) alcohol guidelines were used to determine whether respondents were at low or high risk of alcohol-related harm. Accordingly, low risk was defined as an average of two or fewer standard drinks per day and no more than four standard drinks in a single sitting and high risk was defined as an average of more than two standard drinks per day and/or more than four standard drinks in a single sitting. In addition, a further “very high risk” category was constructed that included individuals who consumed 11+ standard drinks in a single sitting at least once per month. This level of intake (110 g of alcohol) is associated with substantial risk of alcohol dependence and abuse (Greenfield et al., 2014).
Engagement in PBSs was also assessed as per the items used in national alcohol intake surveys (AIHW, 2011, 2014), with respondents asked how often they engaged in the following five strategies: Count the number of drinks you have, Deliberately alternate between alcoholic and nonalcoholic drinks, Make a point of eating while consuming alcohol, Quench your thirst by having a nonalcoholic drink before having alcohol, and Refuse an alcoholic drink you are offered because you really do not want it. Frequency of engagement in each behavior was rated on a scale of 1 (Never) to 5 (Always). A composite score was generated by calculating the grand mean of all items. Cronbach’s alpha was used to assess the internal consistency of the five PBS items selected for inclusion in the study, resulting in a coefficient of .76. In accordance with existing guidelines (George & Mallery, 2003; Kline, 2011), this was deemed adequate.
As per MacKinnon, Nohre, Pentz, and Stacy (2000) and Nohre, MacKinnon, Stacy, and Pentz (1999), beliefs about the proximal risks associated with alcohol consumption (i.e., those relating to negative outcomes that are more likely to occur in the short term) were assessed by asking respondents: “Can drinking alcohol impair your ability to work with machinery?” and “Can drinking alcohol impair your ability to drive a car?” Response options ranged from 1 (No, not at all) to 5 (Yes, definitely). As per Pettigrew et al. (2016), a composite “proximal health risk” belief score was created by calculating the grand mean of both items. As per Kozup, Burton, and Creyer (2001), beliefs about the distal risks associated with alcohol consumption (i.e., those associated with longer term health problems) were assessed by asking respondents to indicate whether they perceived alcohol consumption to be favorable or unfavorable for heart disease, high blood pressure, cancer, stroke, and liver damage. Response options ranged from 1 (Unfavorable) to 5 (Favorable), with items reverse-scored for analysis purposes. A composite “distal health risk” belief score was created by calculating the grand mean of all five items (as per Pettigrew et al., 2016). General beliefs about alcohol were assessed by asking respondents “Overall, what is your attitude or opinion about alcohol consumption?” (5-point response scale Extremely negative to Extremely positive; Peters et al., 2007) and “Do you consider alcohol to be part of a healthy diet?” (5-point response scale No, not at all to Yes, definitely; Kozup et al., 2001).
Statistical Analyses
Descriptive analyses were conducted to calculate the degree to which respondents engaged in each PBS. Pearson chi-square analyses were used to compare engagement in PBSs by risk level, with a Bonferroni-adjusted alpha level of .016 used to control for the familywise error rate and determine significance.
Univariate regression analyses were conducted to identify factors associated with higher and lower levels of enactment of specific strategies and the associated implications for alcohol consumption. Further univariate regression analyses were then conducted to identify variables that were significantly associated with average weekly alcohol consumption. Factors entered as independent variables were gender (1 = male, 2 = female), age, SES (derived from postcode), education (1 = non-tertiary educated, 2 = tertiary educated), household income, overall attitude to alcohol, whether alcohol is considered part of a healthy diet, and beliefs about the potential proximal and distal risks of alcohol consumption. Use of individual PBSs and average engagement across all five PBSs were also entered as factors associated with consumption.
Structural equation modeling was then used to examine the adequacy of a model linking predictors of use of PBSs with predictors of alcohol consumption. Of particular interest was whether use of PBSs influenced alcohol consumption over and above other potential influencing factors and whether there were any indirect effects of the included variables on reported consumption. Several fit indices from the maximum likelihood estimator output were inspected (model chi-square, Tucker–Lewis Index [TLI; ≥ 0.95], Comparative Fit Index [CFI; ≥ 0.95], the root mean square error of approximation [RMSEA; ≤ 0.06] and the standardized root-mean-square residual [SRMR; < 0.08]). This was conducted in Mplus 7.4 (Muthén & Muthén, 1998–2015).
Results Prevalence of Use of PBSs by Alcohol Consumption Risk Status
Respondents’ reports of the extent to which they use the included PBSs are shown in Table 2. Fewer than half of the respondents reported regularly using most of the strategies, with the average usage rate across all strategies being around one third of respondents. For all the strategies, reported usage was lower at higher levels of alcohol-related risk. Of particular note is that on average only half the sample reported refusing drinks they did not want, ranging from one third of high-risk drinkers to two thirds of low-risk drinkers (p < .001).
Engagement in Protective Behavioral Strategies by Risk Status
Factors Associated With PBS Use and Associated Levels of Alcohol Consumption
Univariate regression analyses identified gender, age, tertiary education, attitude to alcohol, and proximal risk beliefs as factors significantly associated with use of PBSs. Univariate regression analyses identified use of PBSs, gender, age, tertiary education, household income, SES, attitude to alcohol, proximal harm beliefs, and belief that consumption of alcohol is part of a healthy diet as factors significantly associated with alcohol consumption.
Based on these results, a model was created that linked use of PBSs at an aggregate level and its predictors with reported alcohol consumption and its predictors (see Figure 1). The model fit the data well with a nonsignificant model chi square, χ2(3) = 7.06, p = .070. All other fit indices met criteria for adequate to excellent fit (χ2/degrees of freedom = 2.35, TLI = 0.95, CFI = 0.99, RMSEA = 0.03 [90% CI = 0.00, 0.05], SRMR = .01). The model accounted for 5% of the variance in engagement in PBSs and 19% of the variance in alcohol consumption.
Figure 1. Path analysis (with standardized regression coefficients and standard errors) of factors affecting engagement in PBSs and alcohol consumption. Nonsignificant paths are depicted by the dashed lines. * p < .05. *** p < .001.
Standardized coefficients and unique errors associated with the observed variables are included in Figure 1. All independent variables other than overall attitude to alcohol (depicted by the dashed line in Figure 1) emerged as significant factors associated with PBS use, with gender exhibiting the largest standardized regression coefficient. PBS use, gender, age, tertiary education, and overall attitude to alcohol emerged as significant factors associated with alcohol consumption. Of these, use of PBSs exhibited the largest standardized regression coefficient.
To examine the mediating relationships proposed by this model, a test of indirect effects was conducted as per Baron and Kenny (1986). All of the indirect effects emerged as significant: gender to alcohol consumption via engagement in PBSs (b’ = −0.05, SE = 0.01, z = −6.30, p < .001), age to alcohol consumption via engagement in PBSs (b’ = 0.02, SE = 0.01, z = −2.21, p < .001), tertiary education to alcohol consumption via engagement in PBSs (b’ = −0.03, SE = 0.01, z = −4.17, p < .001), and proximal risk beliefs to alcohol consumption via engagement in PBSs (b’ = −0.03, SE = 0.01, z = −3.58, p < .001).
Each of the PBSs was then examined individually. After univariate regression analyses identified factors significantly associated with use of each practice, a path analysis was conducted linking use of each PBS and its predictors with alcohol consumption and its predictors (see Figure 2). Although the chi-square associated with this model was significant, χ2(23) = 50.52, p < .001, other fit indices for this model met the criteria outlined for adequate to excellent fit: χ2/degrees of freedom = 2.20, TLI = 0.97, CFI = 0.99, SRMR = .02, and RMSEA = 0.03 (90% CI = 0.02, 0.04). The model accounted for 22% of the variance in alcohol consumption.
Figure 2. Path analysis (with standardized regression coefficients and standard errors) of factors affecting engagement in each of the PBSs and alcohol consumption. Bolded solid lines indicate the presence of a significant direct and indirect effect from the independent variable to alcohol consumption via the various PBSs. Nonbolded solid lines indicate the presence of a significant direct effect. Dashed lines depict nonsignificant pathways. Directionality is only shown for significant pathways. *** p < .001.
Standardized coefficients and unique errors associated with the observed variables are presented in Figure 2. PBSs were allowed to covary. Gender was found to be associated with all five PBSs, with females reporting greater use compared to males in all cases. Beliefs about the proximal risks associated with alcohol consumption also emerged as a significant factor for all individual PBSs, with the exception of Deliberately alternate between alcoholic and nonalcoholic drinks. The PBS Refuse an alcoholic drink you are offered because you really do not want it had the strongest relationship with alcohol consumption, followed closely by Deliberately alternate between alcoholic and nonalcoholic drinks. Make a point of eating while consuming alcohol was least closely associated with alcohol consumption and was not significant. Unexpectedly, the PBS Quench your thirst by having a nonalcoholic drink before having alcohol had a significant positive relationship with alcohol consumption.
To examine the mediating relationships proposed by this model, a test of indirect effects was conducted as per Baron and Kenny (1986). The bolded solid lines in Figure 2 indicate the presence of a significant direct and indirect effect from the independent variable to alcohol consumption via the various PBSs. There were few distinct patterns in the indirect effects, with the primary points of note being the strong effect of gender and the important role of proximal beliefs in influencing respondents’ use of multiple PBSs.
DiscussionThe results of the present study support previous U.S. studies finding that enactment of various PBSs is associated with lower levels of alcohol consumption (Arterberry et al., 2014; Braitman et al., 2015; DeMartini et al., 2013; Linden-Carmichael et al., 2015; Martens et al., 2007; Napper et al., 2014; Palmer et al., 2010; Prince et al., 2013). A substantial proportion of variance in alcohol consumption (19–22%) was found to be accounted for by the tested models. The results extend previous research and contribute to the very limited body of previous work by demonstrating that this effect occurs in a general population sample of drinkers and is therefore not confined to college students.
Of particular importance is the finding that the tested PBSs demonstrated different degrees of association with alcohol consumption. Previous studies have tended to aggregate PBSs within categories, which prevents identification of relative effects of individual behaviors (e.g., Arterberry et al., 2014; DeMartini et al., 2013; Napper et al., 2014; Treloar et al., 2015). The results of the present study suggest that compared to the other PBSs included in the study, encouraging drinkers to refuse drinks they do not want may have the greatest potential to reduce consumption, along with encouraging alternating alcoholic and nonalcoholic beverages. These two strategies accounted for the most variance in reported alcohol consumption. However, changing these behaviors will be a challenging task given the highly social nature of much alcohol consumption, entrenched rituals of pressing alcohol on others to demonstrate hospitality, and turn-taking in purchasing alcohol for group members when out drinking (Borsari & Carey, 2001; Emslie, Hunt, & Lyons, 2013). Such rituals serve to enhance social bonding and encourage continued drinking regardless of desire for alcohol, thereby reducing the likelihood of individuals engaging in the recommended PBSs.
Of note is that commencing a drinking session by quenching thirst with a nonalcoholic beverage was associated with higher levels of alcohol consumption, indicating that encouraging greater enactment of this particular behavior may be counterproductive. It may be that having a nonalcoholic beverage first is seen by drinkers to justify or ameliorate heavy subsequent drinking. Alternatively, the action of drinking, albeit a nonalcoholic product, may prime further drinking behavior, especially in drinking-focused environments (Aarts, Dijksterhuis, & de Vries, 2001). This may occur via multiple mechanisms. In the first instance, intentionally suppressing the desire to consume alcohol, as would occur when initially selecting a nonalcoholic beverage in an alcohol environment, may increase the accessibility of alcohol-related information in the memory, potentially resulting in increased subsequent alcohol consumption (Palfai, Monti, Colby, & Rohsenow, 1997). Second, craving for alcohol at a point in time is also associated with higher subsequent alcohol intake (McHugh, Fitzmaurice, Griffin, Anton, & Weiss, 2016), indicating that triggering craving by encouraging drinkers in high-alcohol environments to delay drinking commencement may result in unintended consequences.
Gender and age were the demographic variables most strongly associated with both PBS enactment and alcohol consumption. In terms of gender, females were significantly more likely to use all five of the strategies and reported lower levels of alcohol consumption. It thus appears that males would be the most appropriate target for future efforts to increase uptake of the recommended behaviors. The tendency for males in this sample to demonstrate lower rates of enactment of PBSs and higher alcohol intake is consistent with prior research in the United States on college and youth samples (DeMartini et al., 2013; Kelly et al., 2012; Prince et al., 2013), indicating that the results may have potential relevance to other national and subpopulation contexts.
The results for age were mixed in the present study, with older drinkers being more likely to engage in two of the PSBs (eating while drinking and refusing unwanted drinks) but also demonstrating higher overall levels of alcohol consumption. This is likely to reflect riskier but less frequent alcohol consumption among younger people relative to more consistent drinking among older people (AIHW, 2014). This suggests that both older and younger drinkers would benefit from greater awareness of the benefits associated with frequent enactment of PBSs. PBSs are not currently actively promoted in Australia, and greater efforts to increase awareness of and motivation to use PBSs are warranted given low take-up levels of most of the recommended strategies.
The results highlight the need to emphasize the importance of monitoring the number of drinks consumed given the NHMRC guidelines’ reliance on recommended numbers of standard drinks to minimize the risk of short- and long-term harm (Parry, Patra, & Rehm, 2011). Many other countries also express alcohol intake recommendations in terms of number of standard drinks, including the United States (U.S. Department of Agriculture & U.S. Department of Health & Human Services, 2010), the United Kingdom (House of Commons, 2012), Canada (Butt, Beirness, Gliksman, Paradis, & Stockwell, 2011), and various parts of Europe (Mongan & Long, 2015). Around two thirds of drinkers in the present study reported that they do not regularly keep count of the number of drinks they consume, which renders alcohol intake guidelines largely futile for these drinkers. Encouraging self-monitoring thus appears to be a vital first step in working toward greater compliance with the guidelines. This will also entail ensuring drinkers are able to accurately assess a standard drink to enable them to accurately count their drinks, which will constitute a substantial communications challenge given the tendency for drinkers to overpour and underestimate intake (Kerr & Stockwell, 2012).
Several alcohol-related health beliefs were included in this study as possible predictors of PBSs. These included general attitudes relating to the healthiness of alcohol as a product and awareness of specific alcohol-related harms, some of which are more likely to manifest in the short term (e.g., the consequences of drinking while driving or operating machinery) or the longer term (e.g., illnesses such as heart disease and cancer). Of the beliefs tested, those relating to proximal risks emerged as most influential, exhibiting a significant association with four of the five PBSs. Such beliefs are potentially modifiable, suggesting that raising awareness of the proximal risks associated with alcohol consumption may assist in promoting greater enactment of PBSs. This approach would be consistent with learnings from tobacco control, where campaigns focusing on the proximal negative consequences of smoking have proven to be effective (Amonini, Pettigrew, & Clayforth, 2015).
The relatively small proportion of variance in aggregated PBS enactment accounted for by the predictor variables included in the study (5%) needs explanation. Recent research suggests that PBSs are unlikely to be enacted unless group norms are supportive (Previte, Russell-Bennett, & Parkinson, 2015). In heavy drinking cultures, group norms favor excessive consumption (Castro, Barrera, Mena, & Aguirre, 2014; Jones, 2014), indicating that future efforts to increase prevalence of PBSs will need to actively counter these norms. In particular, the tendency of many respondents to regularly accept alcoholic beverages they do not want highlights the importance of specifically addressing this apparently counterintuitive behavior in harm-minimization campaigns. Similar to work that has been done to inform the public of the risks associated with drinking during pregnancy and to promote strategies that pregnant women can use to deflect pressure to consume alcohol (France et al., 2014), the results of the present study suggest that it may be beneficial to undertake interventions designed to promote the rights of individuals to decline offers of alcohol and to provide recommendations for effective refusal strategies.
Limitations and Future Research Directions
The present study has several limitations that could be addressed in future research. In the first instance, cross-sectional data were used to investigate the relationships between (a) various predictor variables and enactment of PBSs and (b) individual and aggregated PBSs and alcohol consumption. Although cross-sectional studies are useful for identifying factors that can be definitively examined in subsequent longitudinal or experimental studies (Jacobi, Hayward, de Zwaan, Kraemer, & Agras, 2004), they cannot determine whether the putative factor or the outcome occurred first. There is therefore a need for longitudinal research that tracks individuals over time to provide more robust evidence of factors influencing PBSs and alcohol intake. Such work would be ideally undertaken using a range of sampling strategies across multiple geographical areas to overcome the limitations to generalizability that are associated with web panels and country-specific data.
Due to the analytical approach of the present study, just five PBSs were selected for inclusion. Future research could include a larger number of PBSs to provide greater insight into the differential effects of different strategies. In particular, the strategies included in the present study belong to the “limiting/stopping” and “manner of drinking” PBS categories, and represent just a small number of possible strategies within each of these categories. Future research could include coverage of a broader range of strategies across and within all three categories: limiting/stopping drinking, modifying the manner of drinking, and serious harm reduction (Martens et al., 2007; Treloar et al., 2015). Further, it has been recently suggested that PBS measures may be improved by asking respondents to report actual numbers of times behaviors are enacted, rather than using the broader frequency response options that have been used in the present study and previous research (Braitman et al., 2015). Future work in this area may incorporate such modified measures to obtain more precise data relating to the extent to which different categories of drinkers engage in specific PBSs.
Although the large sample size and coverage of major demographic groups assists in addressing concerns regarding representativeness, the use of a web panel provider to recruit the sample of drinkers for the present study means population representativeness cannot be assumed. Future research could seek to source representative samples via different forms of recruitment that also access very light drinkers to assess whether PBS engagement plays a role in the drinking strategies of these individuals.
Conclusion
Overall, the present study supports the importance of PBSs in influencing alcohol intake. Different PBSs were found to be associated with varying levels of consumption, illustrating the importance of determining which PBSs should be most actively promoted to the drinking public. One PBS was found to be associated with higher levels of alcohol consumption, further highlighting the need to ensure drinking guidelines are evidence based and are not likely to contribute to alcohol-related harm. The results suggest that refusing unwanted drinks and alternating alcoholic and nonalcoholic beverages could be primary target PBSs in the Australian context, with potential application to other countries sharing similar drinking cultures characterized by peer pressure to consume alcohol and cultural norms associated with heavy drinking during social events (Hogan, Perks, & Russell-Bennett, 2014; Kuntsche, Rehm, & Gmel, 2004; Kuntsche et al., 2014). Males and heavier drinkers are likely to be especially important target groups for these interventions. The low reported frequency of counting drinks highlights the need to also encourage this behavior given the emphasis in current drinking guidelines on monitoring the number of standard drinks consumed.
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Submitted: March 12, 2016 Revised: May 22, 2016 Accepted: June 13, 2016
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Record: 41- Predictors of engaging in problem gambling treatment: Data from the West Virginia Problem Gamblers Help Network. Weinstock, Jeremiah; Burton, Steve; Rash, Carla J.; Moran, Sheila; Biller, Warren; Krudelbach, Norman; Phoenix, Natalie; Morasco, Benjamin J.; Psychology of Addictive Behaviors, Vol 25(2), Jun, 2011 pp. 372-379. Publisher: American Psychological Association; [Journal Article] Abstract: Gambling help-lines are an essential access point, or frontline resource, for treatment seeking. This study investigated treatment engagement after calling a gambling help-line. From 2000–2007 over 2,900 unique callers were offered an in-person assessment appointment. Logistic regression analyses assessed predictors of (a) accepting the referral to the in-person assessment appointment and (b) attending the in-person assessment appointment. Over 76% of callers accepted the referral and 55% of all callers attended the in-person assessment appointment. This treatment engagement rate is higher than typically found for other help-lines. Demographic factors and clinical factors such as gender, severity of gambling problems, amount of gambling debt, and coercion by legal and social networks predicted engagement in treatment. Programmatic factors such as offering an appointment within 72 hr also aided treatment engagement. Results suggest gambling help-lines can be a convenient and confidential way for many individuals with gambling problems to access gambling-specific treatment. Alternative services such as telephone counseling may be beneficial for those who do not engage in treatment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Predictors of Engaging in Problem Gambling Treatment: Data From the West Virginia Problem Gamblers Help Network
By: Jeremiah Weinstock
University of Connecticut Health Center, Farmington, Connecticut;
Department of Psychology, Saint Louis University;
Steve Burton
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Carla J. Rash
University of Connecticut Health Center, Farmington, Connecticut
Sheila Moran
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Warren Biller
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Norman Krudelbach
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Natalie Phoenix
University of Connecticut Health Center, Farmington, Connecticut
Benjamin J. Morasco
Portland Veterans Affairs Medical Center and Oregon Health and Science University, Portland, Oregon
Acknowledgement: We thank the First Choice Health Systems, Inc., West Virginia Problem Gamblers Help Network and their callers for sharing the data that made this manuscript possible. The following grants also supported work on this manuscript: R21-AA017717 (Jeremiah Weinstock); T32-AA07290 (Carla J. Rash), K23-DA023467 (Benjamin J. Morasco).
Pathological gambling is an impulse-control disorder characterized as maladaptive gambling behavior that persists despite its many adverse consequences (American Psychiatric Association, 2000). Individuals with pathological gambling endorse at least five of 10 symptoms related to preoccupation, tolerance, withdrawal, and negative financial and social consequences of gambling. The prevalence of the disorder is approximately 1% of the general population (Petry, Stinson, & Grant, 2005). Individuals with subclinical pathological gambling who endorse three or four symptoms are often called problem gamblers and account for an additional 2–3% of the general population (Shaffer, Hall, & Vander Bilt, 1999). Unfortunately, the vast majority of individuals (80% to 95%) with gambling problems never seek professional help (Slutske, 2006; Suurvali, Hodgins, Toneatto, & Cunningham, 2008). While a proportion of those untreated will recover naturally without professional intervention (Slutske, 2006; Cunningham, Hodgins, & Toneatto, 2009), many continue gambling problematically despite the availability of empirically supported interventions. Engaging problem and pathological gamblers in treatment can reduce the adverse consequences of the disorder.
The treatment options for gambling problems are expanding, and range from brief interventions and internet chat-lines to inpatient and residential treatment (Pallesen et al., 2005; Westphal, 2008). Gambling help-lines are an essential access point, or frontline resource, for those seeking help, as they are advertised widely and accessible. Moreover, this medium overcomes some perceived barriers via convenience and anonymity. While several studies have investigated the demographic characteristics and the relationship between gambling severity and psychiatric comorbidity of help-line callers (Griffiths, Scarfe, & Bellringer, 1999; Ledgerwood, Steinberg, Wu, & Potenza, 2005; Potenza et al., 2004; Potenza, Steinberg, & Wu, 2005), little is known about treatment engagement via gambling help-lines. Two studies have found that the proportion of callers agreeing to a referral to in-person treatment from a gambling help-line vary from less than 50% to as high as 75% (Dickerson, 2004; Shandley & Moore, 2008), and approximately two-thirds of those offered a referral make an appointment. The likelihood that individuals will follow through and attend the appointment is not known.
The decision to seek treatment for gambling problems is multi-faceted and often hindered by perceived barriers. Many problem and pathological gamblers cite financial, social and legal pressures as the reasons why they seek treatment (Pulford et al., 2009). Attitudinal factors (i.e., stigma, shame, desire to handle a problem without professional intervention) and environmental barriers (i.e., availability, costs) are common barriers reported by problem and pathological gamblers (Clarke, Abbott, DeSouza, & Bellringer, 2007; Hodgins & el-Guebaly, 2000; Suurvali, Cordingley, Hodgins, & Cunningham, 2009). Demographic characteristics, including male gender, younger age, and less formal education are also identified as barriers to gambling treatment (Clarke et al., 2007). Evans and Delfabbro (2005) describe the process of help-seeking as “crisis driven,” indicating that individuals seek help when the situation is perceived as dire and treatment is seen as a last resort. Although these factors are associated with treatment attendance and engagement in prior studies, they have not been systematically examined in the context of referral from a gambling help-line. Understanding factors related to treatment engagement from a gambling-helpline is paramount, as help-lines become more prevalent and a primary access point for treatment; a call to a gambling help-line is an unique opportunity to provide services to an individual in need that should not be squandered.
The aim of the present study is to investigate treatment engagement of problem and pathological gamblers following an initial gambling help-line call. Treatment engagement in the context of this study is defined as a two-stage process of (a) agreeing to the referral, and (b) accessing the referred services. Using a sample of gambling help-line callers, this study examines demographic, clinical, and contextual characteristics associated with acceptance and attendance of gambling treatment referrals. Based on research concerning perceived barriers of gambling treatment and studies of help-line initiated referrals for other health-related problems (Curry, Grothaus, McAfee, & Pabiniak, 1998; De Coster, Quan, Elford, Li, Mazzei, & Zimmer, 2010; Gould, Kalafat, Munfakh, & Kleinman, 2007; McAfee, 2007), we expect older individuals, females, and those with higher education to accept and follow through on referrals more often than younger individuals, males, and those with less education. Additionally, we hypothesize that greater problem severity and a history of gambling treatment will predict treatment engagement (Pulford et al., 2009).
Method Participants
Data used in this study were from a total of 3,453 unique callers to the Problem Gamblers Help Network of West Virginia (PGHN) from August 2000 until October 2007. Only data from individuals who were offered an in-person assessment (N = 2,912; 84.3%) were analyzed. Callers who were offered an in-person assessment were predominately those with a gambling problem (98.5%; n = 2,865) or a significant other, spouse, or family member of a person with a gambling problem (1.3%; n = 39). In-person assessments were not offered if (a) the caller was not a West Virginia resident, (b) it was deemed an inappropriate call (e.g., prank) by PGHN staff, or (c) the caller ended the contact prematurely without providing any contact information.
Procedure
The PGHN operates a 24-hour toll-free telephone help-line staffed by trained, licensed clinicians. All help-line staff is credentialed at either the national or international gambling counselor level. The number is advertised throughout West Virginia at various gambling venues via billboards, lottery website, public service announcements, and by stickers placed on slot machines. Callers completed a standardized telephone interview with a clinician. If appropriate, a two-hour in-person diagnostic assessment with a licensed counselor trained specifically to work with gambling problems was offered. For those accepting the referral, a “warm transfer” procedure was used. A local clinician was selected by the caller from a list of PGHN providers, and while the caller was holding, help-line staff called the clinician to schedule an appointment. Attempts were made to schedule the in-person assessment appointment within 72 hr of the call to the help-line. In most cases, the caller had an appointment time and directions before the call ended. Help-line staff made a preappointment reminder call 24 hr before the scheduled appointment. All callers were offered information about local Gamblers Anonymous meetings, the Consumers Credit Counsel, and an information packet about problem gambling.
Individuals accepting the referral to the in-person assessment provided a release of information such that the help-line could track their attendance to the in-person assessment appointment and could gather follow-up information resulting from the in-person assessment. The help-line reimbursed the provider for the in-person assessment, which was provided at no cost to the caller. Use of de-identified data for this study was reviewed and approved by the lead author's university institutional review board.
Measures
A standardized telephone interview assessed demographic information, pathological gambling diagnostic criteria, current gambling behavior and debt, history of prior problem gambling help-seeking, current suicidal ideation, and psychiatric history. Information was not collected in the same order for all callers, but responses were primarily coded in a fixed format response (e.g., yes/no, ordinal categories for levels of debt) and used to provide callers with appropriate referrals. The PGHN performs quality assurance assessments on its counselors to ensure help-line staff is following guidelines and collecting accurate information.
Data Analysis Plan
Univariate analyses examined differences between the groups on demographic and clinical characteristics using chi-square tests for categorical data and ANOVA for continuous data. Two separate binary logistic regression analyses assessed predictors of referral (a) acceptance (coded 0,1; 1 = accepted referral), and (b) attendance (coded 0,1; 1 = attended session). As the analyses were exploratory, all independent variables (IVs) were included in the logistic regressions. We used a hierarchical approach for the regression analyses. Block 1 contained demographic characteristics and Block 2 contained gambling and clinical characteristics, as outlined in Tables 1 and 2.
Demographic Characteristics of Gambling Helpline Callers by Referral Status
Clinical Characteristics of Gambling Helpline Callers by Referral Status
The data had a high percentage of missing data, with only 22% (650 of 2,912) of cases having complete data from all 15 IVs. Ninety percent of the sample (2,621 of 2,912) had missing data on ≤ 5 out of the 15 IVs under consideration. Percent of missing data on each IV ranged from 0–35% (with 9 variables missing < 10%): gender (0.3%), age (8%), marital status (5%), employment (7%), annual income (35%), education (32%), gambling frequency (7%), preferred gambling activity (3%), gambling debt (22%), precipitating problem (5%), prior problem gambling help-seeking (14%), recent suicidal ideation (0%), history of comorbid psychiatric disorders (22%), DSM-IV symptoms (5%), and assessment within 72 hr following call (27%). Dependent variables were 100% observed. The main reason for missing data was the clinician failing to ask the item, which is assumed to be missing at random (MAR; Donders, van der Heijden, Stijen, & Moons, 2006). We examined the data for differences among callers in terms of missing data for each variable; with the exception of a significant association between missingness status and number of DSM-IV pathological gambling symptoms endorsed, no other significant differences were present between the missing data groups on demographic and clinical variables. Rates of missingness were higher for those refusing referral versus those accepting the referral, and for those who did not attend the in-person assessment versus those who did. The fact that missing data status can be predicted by other measured variables indicates that MAR is a reasonable assumption for this dataset.
A multiple imputation procedure was implemented in which missing values for any variable are estimated using existing values from other variables. This method assumes data are MAR, an assumption that is not directly testable (Allison, 2003). Multiple imputation using five or more imputations produces less biased estimates than single imputation strategies or complete case analysis under the MAR mechanism (Schafer & Graham, 2002). Additionally, we note that multiple imputation may produce more accurate estimates than complete case analyses even when data do not satisfy MAR assumptions (Graham, 2009). Finally, we considered imputing more than five datasets similar to that recommended by Graham and colleagues (2007). However, given our large sample size and amount of missing data, estimates suggest little efficiency is gained with additional imputations as power would increase only by 0.003 by doubling the number of imputed datasets.
AMELIA II version 1.2-12 (Honaker, King, & Blackwell, 2009) with R version 2.9.1 (R Development Core Team, 2009) was used for the imputation, with all demographic, clinical, and dependent variables included. In order to ensure a high rate of relative efficiency based upon the amount of missing data (Newgard & Haukoos, 2007), five separate imputed datasets were created using a 9% ridge prior. Ridge priors of ≤ 10% are considered reasonable (Honaker et al., 2009). Nominal variables and most ordinal variables were restricted to integer values; however, ordinal variables that represented a continuous variable (e.g., income, gambling debt) were imputed as continuous variables (Honaker et al., 2009). Diagnostics on the imputed datasets suggested imputations were plausible and stable. Logistic regressions were run separately in the imputed datasets and values from each imputed dataset were combined according to Rubin (1987) as outlined by Newgard and Haukoos (2007), resulting in a single set of regression coefficients, standard errors, and confidence intervals. Model fit statistics, combining across the five imputed datasets, were calculated according to Allison's (2001) formulas.
Two separate logistic regression analyses were conducted to evaluate predictors of (a) accepting the referral and (b) attending the in-person assessment. For these analyses, the following demographic variables were entered in Block 1: gender, age, marital status, annual income employment status, and education. Block 2 contained the clinical variables gambling frequency, preferred gambling activity, gambling-related debt, precipitating problem, prior gambling treatment, suicidal ideation, history of psychiatric comorbidity, and number of DSM-IV pathological gambling symptoms. Pathological gambling diagnostic status was not included in the model as number of DSM-IV pathological gambling symptoms is more informative. All data analysis, aside from the multiple imputation procedure, was completed using SPSS (v.15.0).
Results Demographic and Clinical Characteristics
Approximately 81.5% (n = 2,256) of the sample endorsed five or more DSM-IV symptoms of pathological gambling, indicating a likely diagnosis of pathological gambling, 15.1% (n = 417) of the sample endorsed three or four symptoms indicating problem gambling, and 3.4% (n = 95) endorsed zero to two symptoms. About three-quarters of callers to the help-line (n = 2,215) accepted the referral to the in-person assessment and 24% declined (n = 697). For those accepting the referral, 57.1% (n = 1,220) were scheduled within 72 hr of calling the help-line. As noted in Table 1, univariate analyses found significant demographic and clinical differences between those declining, accepting but not attending, and accepting and attending the referral for the in-person assessment, p < .05.
Referral Acceptance
Table 3 displays the final model for predictors of referral acceptance. Both blocks and the overall model were significant, p < .001. After controlling for all other variables in the model (final block), callers whose preferred gambling activity was categorized as “other” had a significantly decreased likelihood of referral acceptance compared with slot machine players. Individuals who had previously sought help for gambling problems were significantly less likely to accept a referral compared to those who had not previously sought gambling treatment. Married or cohabitating individuals and divorced or separated individuals were significantly more likely to accept the referral compared to individuals with single marital status. Similarly, those whose precipitating problem involved legal or spousal pressures were more likely to accept the referral compared to those whose calls were motivated by financial concerns. History of comorbid psychiatric disorders, greater gambling debt, and greater severity of pathological gambling symptoms were positively and significantly related to referral acceptance.
Odds Ratios of Demographic and Clinical Characteristics on Accepting Referral to In-Person Assessment
Appointment Attendance
Of the 2,215 callers who accepted the in-person assessment referral, 72.1% attended the appointment (n = 1,595), 26.3% did not attend the appointment (n = 582), and for 38 individuals it is unknown whether they attended the appointment (1.7%; excluded from subsequent analyses). Logistic regression assessed the relationship between demographic and clinical characteristics and attendance at the in-person assessment. Table 4 displays the final model for predictors of attending the in-person assessment appointment from the logistic regression analysis. Both blocks and the overall model were significant, p < .001. After controlling for all variables in the analysis (Block 2), females were less likely to attend the in-person assessment. Factors associated with an increased likelihood of attending the in-person assessment included age, education, prior gambling treatment, greater severity of pathological gambling symptoms, and in-person assessments scheduled within 72 hr of the call. Additionally, all precipitating problems with the exception of problem recognition were associated with increased odds of appointment attendance compared to those whose call was precipitated by finances.
Odds Ratios of Demographic and Clinical Characteristics on Attending In-Person Assessment
DiscussionOverall, the West Virginia PGHN was able to facilitate engagement in treatment for about 55% of all calls to the help-line. Over 75% of callers accepted the referral, and of those, 72% attended the in-person assessment. These utilization rates are similar or exceed those typically found for other help-line services and attendance rates of initial appointments for other mental health services, which generally range from 35% to 77% (De Coster et al., 2010; Gould et al., 2007; Hser, Maglione, Polinsky, & Anglin, 1998; McKay, & Bannon, 2004; Sherman, Barnum, Nyberg, & Buhman-Wiggs, 2008).
Administrative aspects of the help-line may have facilitated the attendance rate. The help-line staff is specifically trained in the arena of pathological gambling and in help-line intervention techniques that build rapport while collecting all the pertinent information from the caller. “Warm transfer” procedures were used to facilitate the referral to an extensive list of gambling-specific providers across the state, thereby lowering the barrier of knowing where to get professional help and reducing some constraints of travel and geographic limitations. Other help-lines that use warm transfer procedures have seen increases in referral attendance rates (e.g., Curry et al., 1998; Sherman et al., 2008). Additionally, as demonstrated by this study and others (e.g., Compton, Rudisch, Craw, Thompson, & Owens, 2006), scheduling the appointments within 72 hr of the telephone call greatly increased the likelihood of the individual attending the in-person assessment. Finally, the foot-in-the-door technique of a small request (i.e., attend a single session at no cost) may be associated with increased likelihood of compliance (Dillard, 1991). Overall, the way in which a help-line interacts with its callers impacts referral utilization.
Demographic and clinical characteristics were associated with referral acceptance and attendance. Callers with more severe problems and possibly experiencing coercion, such as legal problems or being compelled to call by a family member, were significantly more likely to accept and attend the referral to an in-person assessment. While coercion is a common factor for seeking treatment (Pescosolido et al., 1998), it does not appear to negatively affect clinical outcome (Snyder & Anderson, 2009; Wild, Cunningham, & Ryan, 2006). Another factor associated with the likelihood of attending the in-person assessment was gender. While more women than men called the gambling help-line and more women than men accepted the referral, women were significantly less likely to attend the in-person assessment. Female pathological gamblers tend to have more disruptive and unstable home environments in comparison to male pathological gamblers (Ladd & Petry, 2002), and certain barriers, such as lack of childcare and transportation, may have more of an impact on women than men, therefore contributing to the lower attendance rate (de Figueiredo, Boerstler, & Doros, 2009).
Unfortunately, the help-line was not able to capitalize on the opportunity presented to all callers. Individuals who declined a referral to services tended to have less severe problems in terms of diagnostic symptoms, debt, and psychiatric comorbidity. These individuals may not recognize their gambling as a problem (i.e., precontemplative stage of change) or desire professional help. Brief telephone interventions or mailed self-help materials may still be appropriate and beneficial for these individuals (Hodgins, Currie, Currie, & Fick, 2009). Overall, 45% of callers did not engage in treatment via the help-line. Smoking cessation quitlines offer a successful model of telephone-based counseling (McAfee, 2007) that gambling help-lines could adopt for those who refuse the referral or do not attend the appointment. Quitlines deliver counseling immediately over the telephone when motivation for help is high, and obstacles for treatment such as the delay in getting an appointment and transportation are removed.
It is interesting to note that individuals who had previously sought help for gambling problems were less likely to accept the referral, but those who did were more likely to attend the in-person assessment than individuals who had not previously sought gambling help. Potential reasons for declining the referral may have to do with prior treatment experiences with a specific provider and/or feeling as if treatment does not work. Conversely, experience with gambling treatment and previously acknowledging the need for help to overcome their gambling problems may reduce or remove these barriers that first-time help-seekers may still experience.
Unfortunately, this investigation provides only a static or episodic view of help-seeking from the perspective of the gambling help-line rather than a dynamic or pathways view. Individuals may have called the help-line and then decided to seek help elsewhere through other resources. It is not known how often this occurred and how successful individuals were in utilizing other resources. Another potential limitation of this study included missing data. We used multiple imputation to overcome this limitation. The use of multiple imputed datasets reduced uncertainty in our logistic regression analyses and allowed full use of the dataset (Donders et al., 2006). All reports of prior help-seeking and psychiatric comorbidity were based upon self-report. No objective or verified reports were obtained in regard to these variables, and either under- or overreporting of these events may have occurred. Lastly, barriers such as distance to the clinic and scheduling availability were not assessed and are potential barriers that impact follow-through with the referral.
In summary, a large percentage of the sample accepted and then attended a gambling treatment referral from a help-line; both demographic and clinical characteristics predicted these outcomes. Understanding factors related to not following through with formal treatment has important implications for the field, because a significant portion of treatment seekers first make contact through help-lines. Women were less likely to attend the in-person assessment indicating additional barriers for these individuals. Conversely, gambling problem severity, coercion, and administrative procedures positively influenced referral acceptance and attendance. Additional research is needed to evaluate whether the factors identified in this study generalize to other settings (e.g., help-lines lacking warm transfer procedures), are associated with long-term treatment adherence, and have an impact on treatment outcomes.
Footnotes 1 Gambling is widely available in West Virginia with lottery, slot machines (casino and non-casino based), and horse racing.
2 The PGHN maintains an extensive network of licensed counselors in order to offer referrals in a caller's local area.
3 In 2001, a self-help workbook was incorporated into the help-line's information packet mailed to callers.
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Submitted: March 17, 2010 Revised: January 10, 2011 Accepted: February 16, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (2), Jun, 2011 pp. 372-379)
Accession Number: 2011-07481-001
Digital Object Identifier: 10.1037/a0023240
Record: 42- Predictors of initiation of hookah tobacco smoking: A one-year prospective study of first-year college women. Fielder, Robyn L.; Carey, Kate B.; Carey, Michael P.; Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012 pp. 963-968. Publisher: American Psychological Association; [Journal Article] Abstract: Hookah tobacco smoking has become increasingly prevalent among American college students over the past decade. Hookah smoking is associated with poor health outcomes and exposes users to high levels of nicotine, carbon monoxide, and smoke. Research on the correlates of hookah use has begun to emerge, but all studies thus far have been cross-sectional. Little is known about hookah use during the transition to college, psychosocial factors related to hookah smoking, or prospective predictors of hookah initiation and frequency of use. This longitudinal cohort study examined risk and protective factors predicting initiation of hookah tobacco smoking during the first year of college. First-year female college students (n = 483; 64% White) provided data on demographic, behavioral, and psychosocial variables and precollege hookah use at baseline; they then completed 12 monthly online surveys about their hookah use from September 2009 to August, 2010. Among the 343 participants who did not report precollege use, 79 (23%) initiated hookah tobacco smoking during the year after college entry. Zero-inflated negative binomial regression showed that alcohol use predicted the likelihood of initiating hookah use; impulsivity, social comparison orientation, and marijuana use predicted the frequency of hookah use. These findings suggest that hookah prevention and intervention efforts may need to address other forms of substance use as well as hookah use. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Predictors of Initiation of Hookah Tobacco Smoking: A One-Year Prospective Study of First-Year College Women
By: Robyn L. Fielder
Center for Health and Behavior and Department of Psychology, Syracuse University;
Kate B. Carey
Center for Health and Behavior and Department of Psychology, Syracuse University
Michael P. Carey
Center for Health and Behavior and Department of Psychology, Syracuse University
Acknowledgement: Kate B. Carey is now at the Department of Behavioral and Social Sciences, Program in Public Health, and the Center for Alcohol and Addiction Studies, Brown University. Michael P. Carey is now at the Centers for Behavioral and Preventive Medicine at The Miriam Hospital, the Department of Psychiatry and Human Behavior, and the Department of Behavioral and Social Sciences, Brown University.
We thank Annelise Sullivan for her assistance with data collection and Jennifer Walsh, PhD, for statistical consultation. This research was supported by grant R21-AA018257 from the National Institute on Alcohol Abuse and Alcoholism to Michael P. Carey.
Hookah, or waterpipe, tobacco smoking involves the passage of smoke through water (in the body of the hookah) before inhalation (Maziak, 2008). Hookah use is associated with reduced lung function (Raad et al., 2011) and increased odds of lung cancer, respiratory illness, and periodontal disease (Akl et al., 2010). Compared with smoking one cigarette, a typical session (45–60 minutes) of hookah tobacco smoking confers a higher nicotine dose, greater carbon monoxide exposure, and almost 50 times the smoke volume (Eissenberg & Shihadeh, 2009).
The prevalence of hookah use has increased among youth worldwide over the past two decades (Akl et al., 2011). In the United States, lifetime hookah tobacco smoking is now almost as common among college students as cigarette smoking (Sutfin et al., 2011). One-third to one half of students report lifetime hookah use, and 10–20% report use during the past 30 days (Eissenberg, Ward, Smith-Simone, & Maziak, 2008; Primack et al., 2008; Sutfin et al., 2011). The increasing prevalence of hookah use and its adverse health effects suggests the need for education and prevention, ideally targeted toward those most likely to take up this practice.
Studies of the correlates of hookah use have focused on demographic factors and substance use behaviors. Hookah smoking is common among Arab Americans (Grekin & Ayna, 2008), but it is less popular among other ethnic minority groups, such as African Americans (Barnett, Curbow, Weitz, Johnson, & Smith-Simone, 2009). Adolescents and college students who smoke cigarettes, use other tobacco products (e.g., cigars), smoke marijuana, and drink alcohol are all more likely to report hookah tobacco use (Eissenberg et al., 2008; Jordan & Delnevo, 2010; Sutfin et al., 2011). Less is known about psychosocial determinants of hookah use, such as personality characteristics. Given the correlation between hookah use and other forms of substance use, one might expect that psychosocial correlates of cigarette smoking and alcohol use might also be associated with hookah use, including some personality traits (e.g., impulsivity), stress, religiosity, and depression (Kashdan, Vetter, & Collins, 2005; Patterson, Lerman, Kaufmann, Neuner, & Audrain-McGovern, 2004; Rigotti, Lee, & Wechsler, 2000).
Psychological theory can help to understand hookah initiation and use. From a substance use perspective, Jessor's (1991) problem behavior theory invokes the notion of risk factors, which might include role models for problem behavior, greater access and opportunity to engage in it (e.g., due to location of hookah lounges in college towns; Barnett, Curbow, Soule, Tomar, & Thombs, 2011), and personal and contextual vulnerability for its occurrence (e.g., peer pressure). Because hookah use often co-occurs with other forms of substance use, it may be part of a cluster of co-occurring substance use risk behaviors. Similarly, from a developmental perspective, Arnett's (2000) theory of emerging adulthood suggests that experimenting with hookah might be part of a normative process of seeking new experiences, as rates of other substance use peak during the 18–25 age range (Arnett, 2005). From both theoretical frameworks, the transition to college is a likely time for hookah initiation given the increased freedom enjoyed by residential students, the popularity of hookah lounges in college towns (Sutfin et al., 2011), the ability of students under age 21 to get into hookah lounges but not regular bars (Barnett et al., 2011), media portrayals of hookah smoking as exotic and trendy (Maziak, 2008), permissive social norms about substance use typical of the college environment (Perkins, Meilman, Leichliter, Cashin, & Presley, 1999), and the developmental task of identity exploration (Arnett, 2005).
Given the paucity of research on the predictors of hookah use, we sought to identify risk and protective factors for hookah initiation and subsequent use. Building on empirical precedent (e.g., Windle, Mun, & Windle, 2005) and theory, we investigated demographics, intrapersonal functioning, values, personality, and other substance use as possible predictors of hookah use among female college students. We expected Black and Asian race, academic achievement, religiosity, health value, and self-esteem to be protective factors for hookah initiation and use. We expected impulsivity, sensation-seeking, depression, anxiety, stress, social comparison, marijuana use, cigarette smoking, and alcohol use to be risk factors for hookah initiation and use.
We focused on females because tobacco use patterns, risk, and protective factors differ by gender (Rigotti et al., 2000). We focused on first-year college students because the transition to college is an important developmental period when risky behaviors, such as alcohol and marijuana use, often increase (Fromme, Corbin, & Kruse, 2008). Our research improves upon prior efforts by (a) using a prospective longitudinal design and (b) assessing frequency (rather than just dichotomous indicators) of hookah use. This design allowed us to identify predictors of hookah initiation and of greater involvement with hookah over one year.
Method Study Design and Procedures
All procedures were approved by the Institutional Review Board. Data were from a larger study, conducted from August 2009 to August 2010 at a private university in upstate New York, on health behaviors, relationships, sexual behavior, and adjustment among first-year college women. Participants for the larger study (n = 483) were recruited via a mass mailing sent to incoming female students who would be at least 18 years old by the start of the study and were not international students or scholarship athletes (excluded because of postal lags and policies of the National Collegiate Athletic Association, respectively). Campus fliers, word of mouth, and the psychology department participant pool were also used to bolster recruitment. Most participants (61%) were recruited from the mass mailing, 28% from the participant pool, and 11% from word of mouth or flyers. During their first three weeks on campus, interested students attended brief in-person orientation sessions, during which research staff explained study procedures and obtained written informed consent, and participants completed the baseline survey.
Participants were also invited to complete 12 monthly follow-up surveys. Invitations were sent by email on the last day of each month with an embedded link to a confidential survey website; the surveys required 10 to 20 minutes to complete, and participants had one week to complete them. Participants who missed surveys were allowed to resume participation with the next survey. Participants received $20 (or credit for one hour of research if from the department participant pool) for the baseline survey, $10 for each of the next 10 surveys, and $15 and $20 for the final two surveys; higher compensation was offered at baseline because that survey was the longest, and for the final two surveys to reduce attrition during the summer.
Measures
All predictors were assessed at baseline unless otherwise noted. Participants provided their age, sexual orientation (reduced to heterosexual or other), race (Asian, Black, White, or other/multiple), and ethnicity (Hispanic). Socioeconomic status (SES) was assessed at wave eight using a 10-point SES ladder (Adler, Epel, Castellazzo, & Ickovics, 2000).
Protective factors
Participants indicated their high school grade point average (GPA) on a 4.0 scale. Religiosity was measured with the global religiosity self-ranking item from the Brief Multidimensional Measure of Religiousness/Spirituality (Fetzer Institute, 1999). Health value was measured with the four-item Health Value Scale (Lau, Hartman, & Ware, 1986). Self-esteem was measured with the 10-item Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965).
Risk factors
Impulsivity and sensation-seeking were each measured using six items (Magid, MacLean, & Colder, 2007) from the impulsiveness and monotony avoidance subscales, respectively, of the Impulsiveness–Monotony Avoidance Scale (Schalling, 1978). Depression was measured with the Patient Health Questionnaire-9 (Spitzer, Kroenke, & Williams, 1999), anxiety with the Generalized Anxiety Disorder-7 (Spitzer, Kroenke, Williams, & Löwe, 2006), and perceived stress with the four-item Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983). Social comparison orientation, or the extent to which individuals compare themselves with others (e.g., peers), was measured with the six items that constitute the ability factor of the Iowa–Netherlands Comparison Orientation Measure (Gibbons & Buunk, 1999).
Substance use during the month before college entry was assessed, and specific anchor dates (i.e., August 1–31) were provided to facilitate recall. Participants indicated the number of days on which they used marijuana, the number of cigarettes they smoked each day in a typical week (summed for a measure of cigarettes per week), and the number of standard drinks they consumed each day in a typical week (summed for a measure of drinks per week).
Hookah use
Precollege hookah use was assessed at baseline; participants indicated how many times they “ever smoked hookah before starting college (before August 26, 2009).” At each follow-up, participants indicated on how many days in the last month they “used hookah to smoke tobacco.” All last-month intervals were specified with anchor dates to facilitate recall.
Because the study spanned a year and involved 13 assessments, 2–18% of participants had missing data on hookah use at each follow-up. To maintain the entire sample, we used multiple imputation, which is preferred over traditional approaches such as listwise deletion (Schafer, 1999). We imputed binary indicators of monthly hookah use (based on all observed data used in the analyses) because it was not feasible to impute count data for the number of days of hookah use. We then created a summary variable by summing the total number of months in which participants reported hookah tobacco smoking during the year-long follow-up (i.e., between waves 2–13); this count data outcome served as a measure of sustained hookah use over time. We imputed five complete datasets (Schafer, 1999) with the R package mi (Su, Gelman, Hill, & Yajima, 2011). Analyses were conducted with all five datasets, and parameter estimates were pooled using the imputation algorithms in Mplus 6 (Muthén & Muthén, 2010).
Analytic Approach
Count data tend to violate the assumptions needed for unbiased ordinary least squares regression (Atkins & Gallop, 2007). However, count regression accommodates non-negative integer outcomes with high positive skew. We first tested each variable as a univariate predictor of hookah initiation using zero-inflated negative binomial (ZINB) regression. Negative binomial regression was more appropriate than Poisson regression because of overdispersion, and ZINB regression was used instead of negative binomial regression because of the proportion (77%) of zeroes as well as a theoretical rationale that two processes likely lead to zeroes (i.e., some participants will never use hookah, whereas some will but simply did not do so during the period we observed). ZINB regression includes a logistic portion predicting nonoccurrence of the outcome and a count portion predicting the frequency of the outcome when it occurs (Atkins & Gallop, 2007). Following recommendations for exploratory multivariate models, univariate predictors with p < .25 were candidates for the multivariate model (Hosmer & Lemeshow, 2000). We entered these candidates into a multivariate ZINB regression, with simultaneous entry of all predictors for both the logistic and count portions of the model. In the interest of parsimony, we removed variables with p > .10 in this model and then calculated the final multivariate model.
ResultsPrecollege hookah use was reported by 29% of participants (n = 140), for whom the mean number of days of use was 7.0 (SD = 11.7, median = 3, range: 1–100). These 140 participants were excluded from the present study of hookah initiation.
Participants were 343 first-year female college students who reported no precollege hookah use. The average age at baseline was 18.1 years (SD = 0.3, range: 18–21), and 96% identified as heterosexual. The racial/ethnic distribution was 64% White, 14% Asian, 11% Black, and 12% other/multiple; 7% identified as Hispanic. Participants completed an average of 10.9 of 12 (SD = 2.3, median = 12) follow-up surveys; response rates ranged from 86–98% during the academic year and 83–90% during the summer afterward. Descriptive statistics for all other predictors are presented in Table 1, along with Cronbach's alpha for all scales.
Descriptive Statistics for Predictors (n = 343)
Seventy-nine participants (23%) initiated hookah use during the year-long follow-up. The average total number of months of hookah use for those who initiated was 2.0 (SD = 1.3, median = 2, range: 1–7). Results from the univariate ZINB regressions appear in Table 2. Based on the univariate results, we entered age, sexual orientation, high school GPA, religiosity, self-esteem, impulsivity, social comparison orientation, marijuana use, and alcohol use as predictors in both portions of a preliminary ZINB multivariate model; next, we removed predictors that were not statistically significant at p < .10 in either portion of the model and reran a more parsimonious final model. In the final multivariate model (see Table 3), alcohol use was the only significant predictor in the zero-inflation portion of the model. As alcohol use increased, the likelihood of being a zero (i.e., nonoccurrence of the outcome) decreased; that is, the more alcohol use participants reported at baseline, the more likely they were to initiate hookah tobacco use. Impulsivity, social comparison orientation, and marijuana use were significant predictors in the negative binomial, or count, portion of the model; the higher these variables, the greater the predicted number of months of hookah tobacco use during the year-long follow-up.
Univariate Predictors of Hookah Initiation (n = 343)
Multivariate Predictors of Hookah Initiation (n = 343)
DiscussionHookah tobacco smoking, which has become increasingly common among college students (Sutfin et al., 2011), is an emerging public health concern (Akl et al., 2010). In this study, almost one third of incoming first-year female college students had smoked hookah before college entry, consistent with the results of a study conducted in 2010 (Smith et al., 2011b) but higher than the 10% prevalence found in three previous studies (Barnett et al., 2009; Jordan & Delnevo, 2010; Primack, Walsh, Bryce, & Eissenberg, 2009). The higher rates found by Smith et al. (2011b) and in the current study are likely the result of sampling (i.e., our sample comprised first-year college students, who would have had more time and opportunities to use hookah than 9th–12th grade students sampled in earlier studies) and an ongoing cultural trend; that is, prevalence rates from these two recent studies (data collected in 2010) are higher than rates obtained in studies with data collection in 2005, 2007, and 2008. These and other studies suggest that the popularity of hookah has increased over the past few years; indeed, rates of hookah use among adults in California increased by 40% from 2005 to 2008 (Smith et al., 2011a).
We found that 23% initiated hookah tobacco smoking during their first year of college. Among women who initiated hookah smoking during the study, 45% reported use in only one month, suggesting that many young women who try hookah will experiment with it but not become frequent or regular users. The low-level involvement of many hookah users is consistent with the assertion that substance use is experimental for most emerging adults (Arnett, 2005). More concerning from a health perspective are women who report frequent use. Because hookah smokers ingest nicotine, report increased pleasant subjective effects, and exhibit decreased tobacco abstinence symptoms (Maziak et al., 2009), abuse and dependence are possible.
The prospective design of this study allowed us to examine a variety of demographic, behavioral, and psychosocial predictors of hookah initiation. The strongest risk factors were alcohol and marijuana use; alcohol use predicted initiation, and marijuana use predicted frequency of use. These results corroborate prior research showing strong correlations between hookah and other substance use (e.g., Sutfin et al., 2011) and support the hypothesis of a cluster of substance use behaviors put forth in problem behavior theory (Jessor, 1991). Explanations for the connections among these forms of substance use may include the social aspect, predisposing personality traits, and the (“deviant”) act of smoking. Moreover, youth may have “socially organized opportunities to learn risk behaviors together and normative expectations that they be performed together” (Jessor, 1991, p. 600). Different forms of substance use may also co-occur because they have similar functions (e.g., coping, social affiliation, hedonistic benefits). Prevention and intervention efforts targeting hookah use will need to take into account the probable co-occurrence of multiple forms of substance use (Jessor, 1991).
Relatively few psychosocial risk or protective factors predicted hookah use. Impulsivity and social comparison orientation were positively associated with frequency of hookah use. When presented with the opportunity to use hookah, impulsive students may acquiesce to social influences more easily. Students who compare their behavior with others' may be affected by perceived social norms (Perkins et al., 1999), especially in settings where hookah use is more prevalent and hookah lounges provide more access. College students overestimate the degree to which their peers use substances; only 9% of students used hookah in the last month, but 68% perceived that the typical student had (American College Health Association [ACHA], 2011).
Limitations of the Research
Recruitment from one university may limit the generalizability of our findings; however, the racial/ethnic distribution of our sample is equivalent to that of national samples of female students (e.g., ACHA, 2011). Future research should include men and study gender differences. Our sample had low rates and frequencies of cigarette smoking. Research should be conducted with a sample in which there is higher rates of, and more frequent, cigarette smoking. Although we improve on past research by using continuous rather than dichotomous indicators of hookah use, our outcome was number of months of use rather than a more sensitive daily measure. We assessed only a subset of possible predictor variables and followed participants for only their first year of college. Finally, given the state of the field, our analyses were necessarily exploratory.
Strengths of the Research
To our knowledge, this is the first prospective study of the predictors of hookah initiation. We surveyed almost 500 women about their hookah use during a key developmental period, the transition to college. Patterns of use of other tobacco products differ by gender (Rigotti et al., 2000), so it is useful and novel to investigate hookah use among women. We addressed a gap in the literature by investigating psychosocial predictors of hookah use, and our data clarify the ratio of experimenters to more frequent users. By using ZINB regression to accommodate the count outcome, we determined predictors of any use and of more frequent use.
Implications and Future Research
Because many youth only experiment with hookah a few times and because the strongest predictors of hookah tobacco smoking were other forms of substance use, prevention and intervention efforts should (a) focus on regular users and (b) jointly target hookah, marijuana, and alcohol use to optimize the public health impact. Future research should include males and noncollege attending emerging adults, explore social norms related to hookah use, and investigate why some youth only experiment and others proceed to regular use.
Footnotes 1 Per university data, the sample represented 26% of all incoming female students at the university, with an equivalent ethnic breakdown.
2 Also, two participants had missing data for sexual orientation, one for high school GPA, one for health value, one for social comparison orientation, and 35 for SES; these missing data were also imputed.
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Submitted: June 20, 2011 Revised: February 1, 2012 Accepted: March 20, 2012
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 963-968)
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Record: 43- Predictors of patient retention in methadone maintenance treatment. Proctor, Steven L.; Copeland, Amy L.; Kopak, Albert M.; Hoffmann, Norman G.; Herschman, Philip L.; Polukhina, Nadiya; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 906-917. Publisher: American Psychological Association; [Journal Article] Abstract: This study sought to determine whether select pretreatment demographic and in-treatment clinical variables predict premature treatment discharge at 6 and 12 months among patients receiving methadone maintenance treatment (MMT). Data were abstracted from electronic medical records for 1,644 patients with an average age of 34.7 years (SD = 11.06) admitted to 26 MMT programs located throughout the United States from 2009 to 2011. Patients were studied through retrospective chart review for 12 months or until treatment discharge. Premature discharge at 6- and 12-month intervals were the dependent variables, analyzed in logistic regressions. Clinical predictor variables included average methadone dosage (mg/d) and urinalysis drug screen (UDS) findings for opioids and various nonopioid substances at intake and 6 months. Pretreatment demographic variables included gender, race/ethnicity, employment status, marital status, payment method, and age at admission. UDS findings positive (UDS+) for cocaine at intake and 6 months were found to be independent predictors of premature discharge at 12 months. UDS+ for opioids at 6 months was also an independent predictor of premature discharge at 12 months. Higher average daily methadone dosages were found to predict retention at both 6 and 12 months. Significant demographic predictors of premature discharge at 6 months included Hispanic ethnicity, unemployment, and marital status. At 12 months, male gender, younger age, and self-pay were found to predict premature discharge. Select demographic characteristics may be less important as predictors of outcome after patients have been in treatment beyond a minimum period of time, while others may become more important later on in treatment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Predictors of Patient Retention in Methadone Maintenance Treatment
By: Steven L. Proctor
Department of Psychology, Louisiana State University;
Amy L. Copeland
Department of Psychology, Louisiana State University
Albert M. Kopak
Department of Criminology and Criminal Justice, Western Carolina University
Norman G. Hoffmann
Department of Psychology, Western Carolina University
Philip L. Herschman
CRC Health Group, Inc., Cupertino, California
Nadiya Polukhina
CRC Health Group, Inc., Cupertino, California
Acknowledgement: This project was supported in part by CRC Health Group, Inc. The funding source was not involved in the study design, analysis, interpretation, or writing of the article. All authors contributed in a significant way to the article and have read and approved the final version. One of the authors (Philip L. Herschman) is the former Chief Clinical Officer at CRC Health Group, Inc. None of the authors have any additional real or potential conflicts of interest, including financial, personal, or other relationships with organizations or pharmaceutical/biomedical companies that may inappropriately influence the research and interpretation of the findings.
Opioid use and opioid use disorders remain serious public health concerns. According to estimates from the 2010 National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration [SAMHSA], 2011), approximately 2.2 million persons aged 12 years or older in the U.S. general population met current Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV; American Psychiatric Association [APA], 1994) criteria for an opioid use disorder (i.e., dependence or abuse). Opioids, including prescription pain relievers and heroin, had the second highest rate of past year drug dependence or abuse, behind only cannabis, and rates of current opioid dependence or abuse have also increased since 2002 (SAMHSA, 2011). Opioid use and opioid use disorders have also been associated with a variety of negative outcomes including hospitalization, economic burden, increased vulnerability to other serious medical conditions or infections, additional substance use and psychiatric comorbidity, cognitive impairment, and mortality (Brooner, King, Kidorf, Schmidt, & Bigelow, 1997; Fals-Stewart, 1997; Hulse, English, Milne, & Holman, 1999; Mark, Woody, Juday, & Kleber, 2001; Pilowsky, Wu, Burchett, Blazer, & Ling, 2011; Strain, 2002; SAMHSA, 2008).
In light of the range of impairment and adverse consequences associated with opioid use and opioid use disorders, effective treatment placement and completion is an important goal. One potential treatment option is methadone maintenance treatment (MMT), which is the most widely used form of treatment for problematic opioid use in the United States (Parrino, 2002). Systematic reviews of the vast opioid use treatment literature have shown that maintenance treatment with methadone is associated with increased treatment retention, reduced opioid use, decreased craving, and improved social functioning (e.g., Bart, 2012). The efficacy of MMT in reducing illicit opioid use among opioid-dependent patients is well-documented (for reviews see Amato et al., 2005; Marsch, 1998). However, considerable research has also demonstrated a consistent, statistically significant relationship between MMT retention and various additional favorable outcomes beyond abstinence from opioids (Hartel & Schoenbaum, 1998; Marsch, 1998; Sorensen & Copeland, 2000). For example, not only is the mortality rate for patients receiving MMT substantially lower than that of regular opioid users in the U.S. general adult population not in treatment, but unfavorable or premature discharge from MMT is associated with increased mortality (Caplehorn, Dalton, Cluff, & Petrenas, 1994; Gibson et al., 2008; Hulse et al., 1999; Zanis & Woody, 1998). High rates of MMT attrition are problematic and warrant the need to identify patients at elevated risk for premature discharge. Thus, identification of various pretreatment demographic and clinical variables that may impact MMT retention remains of paramount importance if opioid-dependent patients, treatment providers, and society in general aspire to more favorable outcomes.
A multitude of demographic and individual difference variables have been found to negatively impact various MMT outcomes (Abramsohn, Peles, Potik, Schreiber, & Adelson, 2009; Alterman, Rutherford, Cacciola, McKay, & Boardman, 1998; Avants, Margolin, & McKee, 2000; Goehl, Nunes, Quitkin, & Hilton, 1993; Hser et al., 2011; Lehmann, Lauzon, & Amsel, 1993; Shirinbayan, Rafiey, Roshan, Narenjiha, & Farhoudian, 2010; Simpson, Joe, & Rowan-Szal, 1997; Wong & Longshore, 2008). Select demographic characteristics including male gender, membership to an ethnic-minority group, unmarried, and unemployment have all been found to negatively influence MMT retention (Ball, Lange, Myers, & Friedman, 1988; Deck & Carlson, 2005; Del Rio, Mino, & Perneger, 1997; Hser, Anglin, & Liu, 1990; Judson & Goldstein, 1982; Mancino et al., 2010; Saxon, Wells, Fleming, Jackson, & Calsyn, 1996). Method of payment for MMT services has also been found to result in differential outcome expectations (Maddux, Prihoda, & Desmond, 1994; Murphy & Rosenbaum, 1988). Specifically, patients assigned to a fee-status treatment condition (i.e., required to pay a daily methadone dispensing fee) demonstrated a significantly lower retention rate at 12 months compared with patients who paid nothing for treatment services (34% vs. 54%, respectively; Maddux et al., 1994). Patient fees have long been considered one of the major barriers to MMT (Anglin, Speckart, Booth, & Ryan, 1989; Muhleisen, Clark, Teo, & Brogan, 2005) and the inability to fund one’s own treatment services has been associated with increased admission delays to outpatient MMT (Gryczynski, Schwartz, Salkever, Mitchell, & Jaffe, 2011). Thus, consideration of select pretreatment patient characteristics at treatment admission including method of payment appear to be a requisite for future research efforts aimed at identifying patients at elevated risk for poor MMT response.
One demographic variable in particular that has consistently been found to predict premature discharge from MMT is age, with younger patients evincing higher rates of attrition, for up to 2 years following MMT admission in some studies (Ball et al., 1988; Brown, Watters, Iglehart, & Aikens, 1982; Deck & Carlson, 2005; MacGowan et al. 1996; Magura, Nwakeze, & Demsky, 1998; Mancino et al., 2010; Saxon et al., 1996; Strike et al., 2005; Torrens, Castillo, & Perez-Sola, 1996). However, with the exception of age, many studies investigating pretreatment demographic predictors of premature MMT discharge have failed to identify variables that reliably predict MMT retention, presumably due to the relatively small samples and/or the brief and variable follow-up periods utilized. The limitation pertaining to sample size is particularly salient given small sample sizes have the potential to result in marginally significant effect sizes and may have an additional impact when there is multicollinearity among predictor variables. Furthermore, many of the estimates relating to the various identified demographic predictors of MMT discharge have been imprecise and tend to account for only a fraction of the variance. In light of these disparate findings and methodological constraints, additional research is warranted.
Beyond pretreatment demographic predictor variables, several clinical variables including opioid and nonopioid substance use both prior to and during MMT, as well as average daily methadone dosage, have been found to predict MMT retention. For instance, greater opioid use history in terms of years of use prior to MMT has been found to predict retention, whereas continued use of opioids at 3 months following MMT admission has been shown to significantly predict attrition (Brown et al., 1982; Del Rio et al., 1997; MacGowan et al., 1996). Ongoing use of alcohol and cocaine following MMT admission have also been found to negatively impact retention rates (Brands et al., 2008; Brown et al., 1982; Judson & Goldstein, 1982; Magura et al., 1998; Torrens et al., 1996). Another important clinical variable relates to the type and intensity of MMT services (i.e., appropriate methadone dosage indicated for long-term retention). In fact, accumulating evidence points to the value of higher methadone dosage prescription practices, with dosages between 80 and 100 mg/d typically found to be more effective than lower dosages (e.g., in the range of 60–80 mg/d) in retaining patients (Faggiano, Vigna-Taglianti, Versino, & Lemma, 2003; Ling, Wesson, Charuvastra, & Klett, 1996; Maremmani, Pacini, Lubrano, & Lovrecic, 2003; Strain, Bigelow, Liebson, & Stitzer, 1999; Torrens et al., 1996). Methadone dosages greater than or equal to 100 mg/d have also been shown to result in favorable treatment outcomes with regard to MMT retention compared with lower dosages (Peles, Linzy, Kreek, & Adelson, 2008). Recent findings from a meta-analysis of 18 randomized controlled trials investigating the influence of different dosage ranges on MMT retention rates suggest that favorable outcomes may also be achieved with dosages greater than or equal to 60 mg/d relative to dosages less than 60 mg/d (Bao et al., 2009). Specifically, across dosing strategies (i.e., flexible vs. fixed), 60+ mg/d was associated with greater retention than dosages < 60 mg/d at both 3–6 months (62.5% vs. 50.6%, respectively) and 6–12 months (57.0% vs. 42.5%, respectively). However, it is notable that approximately half of patients maintained on < 60 mg/d were retained through 6 months, and nearly as many were retained in treatment through 12 months. Thus, although dosages in the 60+ mg/d range appear indicated, methadone dosage guidelines, practices, and subsequent retention rates vary and suggest the need for future work.
In sum, increasing rates of opioid use disorders coupled with a resultant public health concern warrant further investigation to determine significant predictors of MMT outcomes and identify patients at elevated risk for poor treatment response at 6 and 12 months. In general, a large number of studies have failed to identify robust pretreatment demographic and clinical predictors of MMT attrition. Studies reporting significant independent predictors of outcome, although promising, require replication in a well-powered investigation. Furthermore, many studies have included relatively small samples and/or brief or limited follow-up periods, and some have relied on self-reported indices of illicit drug use. The limitation pertaining to sample size appears nearly universal across studies and is particularly salient given the potential to result in marginally significant effect sizes. Further, although it is widely accepted that MMT retention is a function of methadone dosage, additional work is warranted to confirm the appropriate dosage range indicated for favorable treatment response. Given these issues, the present study sought to replicate and extend previous findings in an effort to fill the apparent gaps in the MMT research literature using data from a large, multisite MMT population.
The present retrospective longitudinal study has two aims. The first is to assess the impact of select demographic and clinical variables on premature patient discharge at 6 and 12 months. We tested this by identifying significant predictors of treatment discharge after adjustment for relevant variables to determine the effects of both pretreatment demographic variables and treatment performance variables (i.e., urinalysis drug screen [UDS] findings for opioids, cocaine, amphetamines, benzodiazepines, and cannabinoids at intake and 6 months) on attrition at the two follow-up intervals (i.e., 6 and 12 months). The second aim is to replicate prior work in an effort to best delineate the average daily methadone dosage most prudent for favorable treatment response at 6 and 12 months. We tested this in two ways. First, we tested whether patient retention could be predicted by six a priori average daily methadone dosage categories (i.e., 10.1–60.0 mg, 60.1–120.0 mg, 10.1–80.0 mg, 80.1–120.0 mg, 60.1–80.0 mg, and 80.1–100.0 mg), analyzed in logistic regressions. Second, we conducted a bivariate correlation to determine the relationship between average daily methadone dosage prescribed throughout the course of treatment (when examined as a continuous variable) and length of stay (LOS) in MMT. It was hypothesized that a higher average daily methadone dosage would be associated with increased retention.
MethodDemographic and clinical data for the present study were derived from patient records utilizing the management information system of a large U.S. health care provider. A total of 9,212 active and discharged patients admitted to a CRC Health Group-operated substance use treatment program during the period of January 1, 2009 through April 30, 2011 were initially identified based on the following specified inclusionary criteria: (a) minimum length of stay of 15 days; (b) presented for medication-assisted maintenance treatment (as opposed to temporary placement or detoxification); and (c) received methadone (as opposed to one of two buprenorphine formulations). However, only those patients for whom complete demographic data were available (i.e., gender, race/ethnicity, employment status, age, and marital status) were included in the final dataset. The largest proportion of cases were excluded due to missing employment status data (n = 5,408). Next, cases with missing marital status data were excluded (n = 1,375), followed by those with missing or unknown data relating to the reason for treatment discharge (n = 754). In addition, one transgendered patient was excluded. Further, to define reliable measures using aggregated patient data, we followed the recommendation of Simpson et al. (1997), and excluded treatment programs for whom relatively small patient sample sizes were found (i.e., only programs including 50 or more patients were selected); which resulted in a net sample of 1,644 patients. The final sample was comprised of all remaining patients admitted to 26 treatment programs located throughout the United States (e.g., California, Oregon, Virginia, Louisiana, West Virginia, North Carolina, Kansas) during the aforementioned observational period. Given that the 26 treatment programs utilized in the present study were operated by the same national health care provider, all programs followed similar MMT practices as outlined in a common Policy and Procedures manual.
Patients were studied through retrospective electronic chart review for 12 months or until treatment discharge; whichever came first. Our rationale for following patients through the a priori 12-month observational period is consistent with the standard timeframe generally examined in MMT retention research (e.g., Deck & Carlson, 2005; Del Rio et al., 1997; Lehmann et al., 1993). Although there remains disagreement regarding the most appropriate duration of treatment, which depends largely on both the individual patient and the specific goals of treatment, 12 months has commonly been accepted as the minimum timeframe necessary to achieve clinical benefit for most MMT patients (Moolchan & Hoffman, 1994; Simpson et al., 1997). Accordingly, this treatment goal was explicitly conveyed to all patients upon admission to the 26 MMT programs. It is important to note, however, that in select cases, patients “successfully” completed treatment prior to 12 months. In instances in which patients were able to achieve their treatment goals in a relatively short period of time, the treatment team collaboratively arrived at the decision to discharge them due to successful treatment completion. Release of the de-identified dataset was approved by the CRC Health Group, Inc. Institutional Review Board for use in secondary analyses.
Participants
Demographic and clinical characteristics for the total sample at intake are detailed in Table 1. The total sample was comprised of 1,644 patients (63.1% male) with an average age of 34.7 years (SD = 11.06) and a range of 18 to 74 years; although 40.9% were between the ages of 25 and 34 years. Racial composition was predominately Caucasian (75.0%) and Hispanics constituted the largest ethnic-minority group (18.2%). Slightly more than half (52.0%) of the patients were single at the time of admission, and 29.3% indicated that they were either married or had a “significant other.” More than half (57.0%) of the patients were unemployed, and 39.1% were employed at the time of admission. Regarding payment method for MMT services, approximately three fourths (72.1%) of the sample were classified as self-pay.
Demographic and Clinical Characteristics at Intake
Measures
UDS testing was conducted at the discretion of the various MMT programs for individual treatment planning purposes or, in some cases, as a mandate in partial fulfillment of the terms of a patient’s parole. Thus, testing was performed at various intervals, defined by both the state and type of patient, and the timing and frequency of testing varied across sites. However, standard procedures at all facilities required that a minimum of eight UDS tests be conducted per year for each patient. In fact, despite the variability in UDS testing procedures across sites, the frequency of UDS testing for opioids was quite consistent in that more than 99.4% of active patients received a UDS for opioids at the 6- and 12-month intervals. Similarly, nearly all (99.6%) patients received a UDS for the various nonopioid substance categories at the two follow-up intervals, with the exception of cannabinoids. However, even UDS testing for cannabinoids was performed, on average, 92.2% of the time at the various intervals across MMT sites. The methadone dispensing software utilized by all of the MMT programs identified patients due for a UDS on a specific day on a random interval schedule and the dispensing of an individual patient’s prescribed methadone dosage was contingent on UDS submission. Collection of specimens was observed via nonrecording camera observation in accordance with each respective program’s state requirements to ensure authenticity. The type of testing performed and the panel chosen was dictated by the state’s requirements, the certification of the program, and the compliance requirements of the individual facility. Thus, upon request, specimens were subjected to an initial Immunoassay screen to assess for recent use of methadone, alcohol, amphetamines, barbiturates, benzodiazepines, cannabinoids, cocaine, heroin, and oxycodone. Immunoassay class results for the various substances at intake and 6 months were utilized as the predictor variables, analyzed in logistic regressions for the present study’s analyses.
Data Analyses
Patient retention in MMT was the outcome variable of interest. The term “retention,” as it is presented in the context of the current investigation, is defined as the proportion of active patients at the 6- and 12-month follow-up interval. Conversely, treatment attrition (or premature treatment discharge) refers to any situation in which patients are prematurely discharged from treatment prior to the two follow-up intervals, irrespective of the specific reason, and encompasses both patient- and organizational-level factors. That is, in the instance of patients discharged due to financial constraints or against medical advice, treatment discharge may be considered a patient-level variable, while patients discharged due to administrative reasons (e.g., not participating in treatment, failure to comply with program policies) would suggest treatment discharge to be an organizational-level variable. Patients were dichotomized as either treatment successes or premature treatment discharges at the 6- and 12-month follow-up intervals based on their LOS in treatment (measured in days). Thus, patients with an LOS > 179 and 364 days at the 6- and 12- month intervals, respectively, were classified as treatment successes. In an effort to avoid artificially inflating the attrition rate at 6 and 12 months, patients who successfully completed treatment or were transferred to another MMT facility (presumably to a higher level of care) prior to the two follow-up intervals were excluded and subsequently not classified as premature treatment discharges at each respective follow-up interval. This procedure revealed that 12.6% (n = 207) and 13.7% (n = 225) of the total sample completed treatment or were transferred, respectively, during the 12-month observational period. Patients discharged after 179 days due to successful treatment completion or transfer to another MMT facility, however, were still classified as treatment successes at 6 months.
All UDS findings (i.e., obtained at intake and 6 months) were dichotomized to indicate the detection of the presence or absence of the various substances for which a UDS was administered at each respective interval. Alcohol and barbiturates were detected in less than 2% of cases at intake, so these substances were not considered as potential individual predictors of treatment attrition. Similarly, all patients were positive for methadone at the various intervals following MMT admission, so this variable was excluded from the respective models. A variable was constructed based on UDS findings for each of the specified substances at intake and 6 months, and included all findings from which a UDS was administered within 15 days of each interval for the various substances. For example, for the 6-month cocaine UDS variable, all patients administered a UDS for cocaine between 165 and 195 days following treatment admission were included. An algorithm was also utilized to place patients into a composite “opioids” UDS category based on UDS findings for both heroin and oxycodone at the 6-month interval. Thus, if a patient produced a positive UDS finding for heroin, oxycodone, or both at 6 months, they received a positive UDS designation when grouped in the composite opioids UDS category. The algorithm utilized to classify patients at intake, however, included positive findings for methadone in addition to heroin or oxycodone given methadone may have been used recreationally prior to MMT admission.
Patients were grouped into six a priori, nonmutually exclusive categories based on average daily methadone dosage received throughout the duration of their treatment (i.e., 10.1–60.0 mg, 60.1–120.0 mg, 10.1–80.0 mg, 80.1–120.0 mg, 60.1–80.0 mg, and 80.1–100.0 mg). The rationale for this categorization procedure was to first examine the differential outcome expectations for patients receiving an average methadone dosage greater than 60.0 mg/d relative to those receiving 60.0 mg/d or less. Second, in an effort to isolate the specific methadone dosage range associated with increased retention in MMT, those patients receiving greater than 60.0 mg/d were divided into two groups representing those receiving greater than 80.0 mg/d (but less than 100.1 mg/d) and those receiving less than 80.1 mg/d (but still greater than 60.0 mg/d). The methadone dosage categories described here are consistent with those commonly examined in the MMT retention literature (e.g., Bao et al., 2009; Magura et al., 1998; Peles et al., 2008; Strain et al., 1999; Torrens et al., 1996).
Separate hierarchical binary logistic regression models were fitted to the data to test the hypotheses regarding whether premature MMT discharge could be predicted at the 6- and 12-month intervals by: (a) pretreatment demographic variables alone; and (b) pretreatment and in-treatment clinical performance variables (i.e., UDS findings for cocaine, amphetamines, benzodiazepines, and cannabinoids obtained at intake and the 6-month interval) after adjustment for relevant demographic variables and average daily methadone dosage received throughout the duration of treatment. The dependent variable for the logistic regressions was a binary variable coded as 1 if discharged due to various reasons (i.e., administrative, financial, or medical) or against medical advice prior to 6 or 12 months and 0 if the patient was still enrolled in MMT at the various a priori follow-up intervals (i.e., 6 and 12 months); this provided for a measure of premature treatment discharge. Logistic regressions involving average daily methadone dosage as a predictor, however, utilized a binary dependent variable indicative of MMT retention (i.e., coded as 1 = enrolled in MMT at the two a priori follow-up intervals and 0 = discharged). Inclusion of relevant demographic variables in the various models was determined based on significant findings from chi-square analyses. Goodness-of-fit statistics were examined to assess the fit of each respective logistic model against actual outcome (i.e., whether patients were classified as premature treatment discharges at 6 and 12 months). One inferential test (i.e., Hosmer-Lemeshow) and two additional descriptive measures of goodness-of-fit (i.e., R2 indices defined by Cox & Snell [1989] and Nagelkerke [1991]) were utilized to determine whether the various models fit to the data well. Finally, a positive UDS finding for opioids at intake was not included as a predictor variable given a positive finding for this substance was nearly universal for the total sample at intake and the resultant lack of variance precluded identifying a relationship with premature treatment discharge at the 6- and 12-month interval.
Separate binary logistic regressions were also conducted to further assess the impact of various pretreatment demographic characteristics on 6- and 12-month retention rates, as well as delineate the average daily methadone dosage category most prudent for increasing retention in MMT at 6 and 12 months. In terms of racial/ethnic groups, only two groups (i.e., Caucasian and Hispanic) were of sufficient size to justify inclusion in the models as predictor variables. Thus, the total sample was dichotomized in order to classify patients based on group membership (Hispanic vs. non-Hispanic, Caucasian vs. non-Caucasian). Logistic regressions involving these two binary categorical variables were utilized to ascertain whether particular racial/ethnic groups were more strongly associated with premature MMT discharge at the 6- and 12-month intervals. A similar procedure was performed for the patient payment method, marital status, and employment status pretreatment variables.
Results UDS Findings and Retention Rates
Based on UDS findings at intake, nearly all (93.7%) of the patients produced a positive finding for opioids (i.e., heroin, oxycodone, or methadone). The remaining positive UDS findings obtained at intake that predominated were as follows: cannabinoids, 31.7%; benzodiazepines, 26.5%; cocaine, 11.8%; and amphetamines, 10.8%. Examination of the UDS findings at 6 months revealed that only 6.9% of the patients produced a positive finding for opioids (i.e., heroin or oxycodone). Regarding the remaining UDS results at 6 months, 4.3% produced a positive UDS finding for only one nonopioid substance, and 2.5% were found positive for more than one nonopioid substance. Specifically, 4.8% were positive for benzodiazepines, 3.7% for cannabinoids, 1.9% for cocaine, and 2.5% for amphetamines.
With respect to the observed retention rates, 46.8% of patients were retained at 6 months and 20.3% were retained at 12 months. At 6 months, the percentages regarding the total number of patients classified as premature treatment discharges due to the various specific reasons for discharge were as follows: 49.1%, against medical advice; 25.7%, administrative discharge; 23.9%, financial constraints; and 1.3%, medical discharge. However, it is important to note that as discussed earlier, patients discharged due to successful treatment completion (n = 99) or transfer to another MMT facility prior to the 6-month interval (n = 101) were excluded in an effort to avoid inflation of the attrition rate. Similar to the 6-month estimates, the percentages and specific reasons for discharge regarding the total number of premature treatment discharges at 12 months were as follows: 47.1%, against medical advice; 27.1%, administrative discharge; 24.3%, financial constraints; and 1.5%, medical discharge. Over one third of patients were excluded due to successful treatment completion (n = 108) or transfer to another MMT program (n = 124) during the 6- to 12-month interval.
Demographic Variables
Results from separate logistic regressions revealed that the risk of premature MMT discharge at 6 months was significantly higher for Hispanics (OR: 1.37, 95% CI [1.03, 1.81]), Model χ2(1) = 4.738, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), unemployed patients (OR: 1.26, 95% CI [1.03, 1.56]), Model χ2(1) = 4.832, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), and patients not married or having a significant other at intake (OR: 1.27, 95% CI [1.01, 1.59]), Model χ2(1) = 4.143, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), not adjusting for other factors. Patient gender, Caucasian race, age, and method of payment were not found to significantly predict premature MMT discharge at 6 months. At the 12-month interval, the risk of premature discharge was significantly higher for self-pay patients (OR: 1.44, 95% CI [1.08, 1.93]), Model χ2(1) = 6.029, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), male patients (OR: 1.33, 95% CI [1.01, 1.75]), Model χ2(1) = 4.110, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), and patients younger than 35 years of age (OR: 1.36, 95% CI [1.04, 1.79]), Model χ2(1) = 4.831, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), not adjusting for other factors. Employment status, Hispanic ethnicity, Caucasian race, and marital status were not found to significantly predict MMT attrition at 12 months.
In summary, unemployment and being Hispanic increase initial premature discharge risks while being married seems to decrease such risks. However, by the 12-month mark, younger age, male gender, and self-pay status seem to become greater factors in failure to continue in MMT during the second 6 months of treatment.
Clinical Variables
Hierarchical binary logistic regressions were also fitted to the data to assess the impact of various clinical variables on premature MMT discharge at 6 and 12 months after adjustment for relevant covariates (see Table 2). The only intake UDS finding entered into the model that was found to significantly predict premature MMT discharge at 6 months was a positive finding for cocaine, after controlling for employment status, ethnicity, marital status, and average daily methadone dosage, Model χ2(8) = 211.122, p < .001, R2 = .19 (Cox & Snell), R2 = .26 (Nagelkerke). Further, the Hosmer-Lemeshow goodness-of-fit test was insignificant, χ2(8) = 8.401, p > .05, suggesting that the model was fit to the data well. Specifically, patients found positive for cocaine at intake were 1.79 times (95% CI [1.18, 2.72]) more likely to be prematurely discharged at 6 months, compared with patients found negative for cocaine at intake. At the 12-month interval, the only independent clinical variables found to significantly predict MMT discharge were a positive UDS finding for cocaine at intake and a positive UDS finding for opioids at 6 months, Model χ2(13) = 52.605, p < .001, R2 = .17 (Cox & Snell), R2 = .23 (Nagelkerke), after controlling for patient gender, age, method of payment, and average daily methadone dosage. The Hosmer-Lemeshow goodness-of-fit test was also insignificant, χ2(8) = 4.510, p > .05. In fact, patients found positive for cocaine at intake were 3.71 times (95% CI [1.35, 10.17]) more likely, and patients found positive for opioids at 6 months were 2.13 times (95% CI [1.10, 4.12]) more likely to be prematurely discharged at 12 months, compared with patients found negative for cocaine and opioids at intake and 6 months, respectively. The remaining intake and 6-month UDS findings were not found to significantly predict MMT discharge at the 12-month interval. In other words, it appears that a positive UDS finding for cocaine at intake and a positive UDS finding for opioids at 6 months were the only clinical variables found to independently contribute to study outcome (i.e., MMT attrition at 12 months) after adjustment for relevant covariates.
Clinical Predictors of Premature Treatment Discharge at 6 and 12 Months
Average Daily Methadone Dosage
Regarding the average daily methadone dosage prescribed for the total sample, nearly one third (32.2%) of patients were prescribed a dosage between 40.1 and 60.0 mg/d, and nearly as many (29.0%) were prescribed a dosage between 60.1 and 80.0 mg/d throughout the duration of treatment. The balance of the cases was as follows: 40.0 mg/d or less, 20.4%; 80.1–100.0 mg/d, 13.2%; 100.1–120.0 mg/d, 3.7%; and only 25 patients (1.5%) were prescribed an average daily dosage of 120.1 mg or greater.
Results from logistic regressions revealed that patients prescribed an average methadone dosage of 60.1–120.0 mg/d were 4.01 times (95% CI [3.27, 5.10]) more likely to be retained in MMT at 6 months than patients prescribed an average dosage of 10.1–60.0 mg/d, Model χ2(1) = 163.539, p < .001, R2 = .11 (Cox & Snell), R2 = .15 (Nagelkerke). At the 12-month interval, patients prescribed an average methadone dosage of 60.1–120.0 mg/d were 3.58 times (95% CI [2.66, 4.82]) more likely to be retained in MMT than patients prescribed an average dosage of 10.1–60.0 mg/d, Model χ2(1) = 76.397, p < .001, R2 = .06 (Cox & Snell), R2 = .09 (Nagelkerke). Further examination of the specific dosage range most prudent for favorable treatment response found that patients prescribed an average methadone dosage of 80.1–100.0 mg/d were 4.47 times (95% CI [2.93, 6.80]) more likely to be retained in MMT at 6 months than patients prescribed an average dosage of 60.1–80.0 mg/d, Model χ2(1) = 57.621, p < .001, R2 = .09 (Cox & Snell), R2 = .12 (Nagelkerke). Similarly, patients prescribed an average methadone dosage of 80.1–100.0 mg/d were 3.32 times (95% CI [2.23, 4.94]) more likely to be retained in MMT at 12 months than patients prescribed an average dosage of 60.1–80.0 mg/d, Model χ2(1) = 35.127, p < .001, R2 = .06 (Cox & Snell), R2 = .09 (Nagelkerke). When comparisons involved 80.1–120.0 mg/d versus 10.1–80.0 mg/d, the differences in outcome were even more pronounced, such that those in the higher methadone dosage group (i.e., 80.1–120.0 mg/d) were 7.73 times (95% CI [5.49, 10.88]) more likely to be retained in MMT at 6 months than patients in the lower methadone dosage group, Model χ2(1) = 179.994, p < .001, R2 = .12 (Cox & Snell), R2 = .16 (Nagelkerke). At 12 months, patients in the higher methadone dosage group were 6.25 (95% CI [4.57, 8.55]) times more likely to be retained in MMT than patients in the lower methadone dosage group d, Model χ2(1) = 128.894, p < .001, R2 = .10 (Cox & Snell), R2 = .15 (Nagelkerke). Thus, higher average daily methadone dosages were found to predict MMT retention at both 6 and 12 months. Finally, there was a moderate, positive correlation found between average daily methadone dosage prescribed throughout the course of treatment (when examined as a continuous variable) and LOS, r = .357, p < .001, with higher dosages associated with increased retention.
DiscussionThe findings replicate and extend prior work which indicated that various pretreatment demographic and clinical variables were associated with MMT retention. Unlike prior published longitudinal MMT research, however, the present study utilized a substantially larger treatment sample, examined a longer timeframe, and controlled for relevant demographic and clinical characteristics that have the potential to impact outcome. This strategy yielded several important implications in that the present findings revealed that certain pretreatment demographic characteristics were associated with differential outcome expectations at both the 6- and 12-month intervals.
Demographic variables appeared to exert their influences either early, during the first 6 months of treatment, or later during the second 6 months. Membership in an ethnic-minority group (i.e., being of Hispanic ethnicity), unemployment, and not being married or having a significant other were the only significant and independent predictors of premature MMT discharge at 6 months. However, none of these variables were found to predict discharge at 12 months; presumably because these factors had already exerted their influence at the 6-month mark. In fact, examination of the observed 6-month attrition rates for these three predictor variables revealed that 59.6% of Hispanic patients, 55.8% of unemployed patients, and 54.9% of patients not married or having a significant other (i.e., single, separated, divorced, or widowed) had already been discharged from treatment prior to 6 months. Conversely, demographic variables found to predict discharge at 12 months included male gender, method of payment for treatment services (i.e., self-pay), and being younger than 35 years of age. Thus, it appears that select demographic variables may be more important early, during the initial 6 months of MMT, while others may be more important later on in the MMT process.
For instance, with regard to patient employment status and ethnicity, our findings are in accord with prior studies which found that unemployed patients and patients of an ethnic-minority group were more likely to experience a poor outcome with respect to treatment retention (Ball et al., 1988; Hser et al., 1990; Judson & Goldstein, 1982). The finding that unemployment was found to significantly predict premature MMT discharge at 6 months was not surprising given that patient fees represent a major obstacle to successful MMT outcomes (Anglin et al., 1989; Gryczynski et al., 2011; Muhleisen, Clark, Teo, & Brogan, 2005), and the risk of dropout is higher for patients with no stable source of income prior to treatment admission (Del Rio et al., 1997). Additional correlates of unemployment, beyond simply a lack of income, may explain the observed findings considering that unemployed patients often present with co-occurring issues known to impact substance use treatment outcomes (for review see Henkel, 2011). Patients in the present study may have also been unemployed due to any number of potential contributing factors (e.g., a more severe opioid use disorder, lack of transportation, lower motivation), which would undoubtedly create barriers to successfully completing MMT. Regardless of the co-occurring issues and underlying reasons for unemployment, the development of relationships with job placement agencies or the inclusion of vocational promotion and rehabilitation services for appropriate patients at the outset of MMT may be indicated if programs aspire to impact the relatively poor retention rates among unemployed patients.
Further, patients not currently married or having a significant other at treatment admission demonstrated poorer retention in MMT at 6 months. Potential reasons for the differential outcome expectations for married/significant other patients compared with members of the other marital status categories (i.e., single, divorced, widowed, and separated) include several factors found to predict MMT retention (Shirinbayan et al., 2010; Torrens et al., 1996). That is, the presence of more immediate access to a stable social network and additional support in the form of encouragement from their partner, as well as an overall increased level of perceived social support may explain the observed findings. Therefore, ethnic-minority patients, unemployed patients, and those patients not currently married or having a significant other at admission may require additional services from the staff or the consideration of alternative treatment regimens early on in the treatment process to help thwart the problem of MMT attrition. Attention to the unique needs of these subgroups of patients has the potential to improve retention.
Although well-documented in the MMT literature (e.g., Brown et al., 1982; Deck & Carlson, 2005; Hser et al., 1990; MacGowan et al., 1996; Magura et al., 1998; Mancino et al., 2010; Saxon et al., 1996; Strike et al., 2005), the present study also replicated prior work in that younger patients were found to evince a significantly higher rate of treatment attrition at 12 months, which suggests that younger patients may be less prepared for extended treatment. This presumably may be due to younger patients’ lower maturity level or less cumulative substance-related negative consequences in their lifetime relative to older patients. Additionally, the finding that the risk of premature discharge at 12 months was significantly higher for male (OR: 1.33, 95% CI [1.01, 1.75]) than female patients may be indicative of important gender-specific differentials relating to MMT prognostic indicators or it may simply be an artifact of the sample composition. Given that women with more severe substance use problems have traditionally been found to seek treatment less often than men, arguably due to a history of trauma and the presence of more barriers to treatment (e.g., childcare responsibilities, inadequate health insurance), further investigation is warranted (Ashley, Marsden, & Brady, 2003; Hodgins, El-Guebaly, & Addington, 1997).
Together, the various demographic variables found to significantly predict premature MMT discharge at both 6 and 12 months suggest that more intensive and/or supplemental services may be appropriate for select subgroups of patients. Although the predictors of treatment discharge by 12 months (i.e., < 35 years of age, male, and self-pay) may not require immediate attention relative to the 6-month predictors and related constructs (e.g., unemployment, limited support), MMT programs should consider early intervention with members of these select groups if 12-month retention rates are desired. From a clinical standpoint, one potential treatment option would be to incorporate motivational enhancement techniques (e.g., motivational interviewing) into standard treatment programming (Miller & Rollnick, 1991). At the program level (and assuming local resources permit such a strategy), MMT programs may consider determining the composition of select psychotherapeutic groups on the basis of age or gender, and supplementing standard programming with topics or techniques designed to increase treatment engagement. At the individual level, both appointments with counselors or case managers and visits with prescribing physicians also represent a suitable context to elicit motivation from patients at elevated risk for premature discharge, which in turn may improve treatment outcomes.
The finding that patients’ method of payment for MMT services predicted premature discharge at the 12-month interval warrants additional comment. Viewed from a sheer economical perspective, the finding that self-pay status eventually became associated with decreased retention in treatment relative to non-self-pay patients over time is hardly surprising given the cumulative out-of-pocket expenses that self-pay patients would have acquired had they remained in treatment through 12 months. The differential outcomes appear to be more an issue of the apparent inability to sustain payment for treatment services, and suggest that cost may not become a statistically significant treatment barrier to successful outcomes until self-pay patients have been in treatment beyond a minimum of 6 months. The additional finding that an estimated one in four premature MMT discharges at both the 6- and 12-month mark were discharged due to financial constraints further confirms the notion that cost may be a significant barrier to MMT completion. Thus, the observation that self-pay status comes into play after a period of being in MMT suggests that economic factors beyond employment alone may be an impediment to long-term treatment. The data are compatible with the conjecture that those for whom the costs of treatment are an economic strain may be more likely to discontinue their treatment. While the trends for employment and payment source are in the consistent direction, it may be the case that one is more important during the two time intervals.
The general finding that positive UDS results for substances other than opioids at intake are associated with increased attrition risks has implications for treatment planning. However, the most important implication concerns the fact that a positive UDS finding for cocaine at intake and 6 months were both found to independently predict premature treatment discharge, after adjustment for relevant demographic variables and additional UDS findings obtained at intake and 6 months. Specifically, patients found positive for cocaine at intake were nearly two times more likely to be discharged at 6 months and almost four times more likely to be discharged by the 12-month mark. From a clinical standpoint, these findings suggest that MMT programs should allocate time and resources toward the treatment of cocaine use and related problems in addition to opioid dependence, rather than simply focusing on the treatment of opioid-related problems alone. In fact, concomitant cocaine use is common among patients presenting for MMT (Chaisson et al., 1989; DeMaria, Sterling, & Weinstein, 2000) and the inclusion of cognitive–behavioral or reinforcement-based interventions designed specifically for cocaine use into standard MMT practices has been found to positively impact clinical outcomes (Barry, Sullivan, & Petry, 2009; Rawson et al., 2002; Silverman et al., 1998). Thus, MMT protocols which incorporate additional psychosocial approaches for cocaine use may improve patient retention in treatment.
Another key finding is the confirmation of previous work that higher dosages of methadone consistently produce better results. Our findings are consistent with previous research (Bao et al., 2009; Faggiano et al., 2003; Ling et al., 1996; Maremmani et al., 2003; Strain et al., 1999; Torrens et al., 1996) in that higher average methadone dosages were associated with increased retention in treatment. Of particular interest were the 6- and 12-month outcomes when average methadone dosage was dichotomized at 80.0 mg/d (i.e., 60.0–80.0 vs. 80.1 vs. 100.0). Specifically, results from logistic regressions revealed that there was over a fourfold increase in the likelihood of MMT retention for patients prescribed the higher dosage at 6 months, and more than three times as likely to be retained in treatment at 12 months. Therefore, the findings suggest that MMT retention appears to be a function of average daily methadone dosage and support the hypothesis that higher daily methadone dosages may positively impact retention in MMT. Although including average daily dosage as a predictor of MMT outcome in regression models is consistent with previous research, when analyses were conducted with peak methadone dosage as a predictor, the observed findings remained generally the same.
It is important to note, however, that although there may be some pharmacological basis for the observed differential findings, the outcomes are likely to be multiply determined, and as such, require additional discussion regarding alternative interpretations. That is, there may be clinical expectancies and biases operating that are not apparent in the data but that played a role in patient retention in treatment. For instance, the findings regarding the associations between lower dosage ranges and decreased retention may be the product of less treatment engagement as opposed to simply a matter of dosage. That is, given higher dosages of methadone have the potential to attenuate or block the reinforcing effects of opioids (SAMHSA, 2005), patients may have intentionally requested lower dosages in an effort to continue using illicit opioids. Similarly, patients may have been aware of methadone’s relative ease of cessation at lower dosages due to decreased withdrawal symptoms. Considering that many programs are responsive to patient requests for lower dosages, both patient and physician biases—although not apparent from patient data derived from electronic medical records—may be important sources of variance in terms of outcomes, which warrant the need for further investigation.
Study Limitations
The findings from the present study should be considered in light of several limitations that suggest the need for additional work in the area of identifying predictors of MMT retention. First, the present study utilized a predominately Caucasian convenience sample comprised exclusively of patients presenting for long-term methadone maintenance in the United States. Despite the relatively large geographical coverage relating to the MMT programs utilized in the present investigation, some caution is warranted in generalizing the findings to other programs, particularly those serving populations with a more varied racial/ethnic composition. Furthermore, the finding that nearly three fourths of the sample funded their own treatment (i.e., were self-pay) and all 26 MMT programs were “for-profit,” represent another potential limitation pertaining to the generalizability of the findings given estimates from several large-scale MMT studies indicate that generally less than half of patients presenting for MMT are self-pay (Banta-Green, Maynard, Koepsell, Wells, & Donovan, 2009; Bradley, French, & Rachal, 1994). The present study design also consisted of retrospective longitudinal electronic chart review and therefore, warrants further prospective longitudinal work.
Another limitation involved the issue of missing or incomplete demographic data for a sizable number of patients included in the initial data set. That is, in the instance of unavailable data for select demographic variables for a substantial number of patients, it is possible that more complete demographic data might have altered the results; although a larger sample size has the potential to reinforce the present findings as well. Thus, the present findings should be considered as a minimum dataset, consisting of lower bound estimates of demographic predictors of outcome within the current sample. The breadth of clinical data included in the present dataset represents another limitation. Although the present study examined the impact of various UDS findings obtained at various intervals as well as average daily methadone dosage on MMT retention, additional clinical factors found to impact retention, including program philosophy and ancillary services data, as well as extent of prior substance use and treatment admissions history data (Brown et al., 1982; Deck & Carlson, 2005; Saxon et al., 1996), were not included. Moreover, motivation and readiness to change, as well as perceived self-efficacy are important individual difference factors to consider in future work given their influence on MMT retention and various clinical outcomes (Hser et al., 2011; Joe, Simpson, & Broome, 1998; Li, Ding, Lai, Lin, & Luo, 2011; Nosyk et al., 2010; Wong & Longshore, 2008).
Given the large variation in average daily methadone dosage, another limitation is that overall dosage-level recommendations may not provide clinical staff with sufficient information to adequately guide treatment practice. Future research should focus on identifying the most effective processes of dosage determination practices (e.g., examination of serum methadone levels; Leavitt, Shinderman, Maxwell, Eap, & Paris, 2000) rather than simply delineating specific dosage levels most prudent for favorable treatment response. However, inclusion of average daily methadone dosage as a predictor of outcome in regression models is consistent with previous MMT research (Hallinan, Ray, Byrne, Agho, & Attia, 2006; Soyka et al., 2008). Consideration of various individual difference (e.g., sexual abuse history, mental health conditions) and treatment delivery (e.g., guideline adherence, tendency to encourage dosage reductions) factors found to correlate with the dosage of methadone at which patients achieve positive clinical outcomes is also a requisite for future studies (Trafton, Minkel, & Humphreys, 2006). Finally, the observed findings are predictive associations and as such, causal interpretations cannot be assumed.
Conclusions
As the number of U.S. adults receiving treatment for opioid dependence continues to increase annually (SAMHSA, 2011), coupled with the resultant public health concern, the challenge of identifying patients in need of specialized services at the outset of treatment and measures to optimize positive outcomes is of paramount importance. Specific modifications to treatment regimens early on in the process for certain subgroups of patients based on select pretreatment characteristics and intake UDS findings have the potential to forestall premature treatment discharge. Despite the short-term predictive value of select factors at treatment admission, consideration of additional variables might also serve as equally important indicators to guide subsequent treatment planning beyond a minimum interval of time. In sum, the current findings provide indications that consideration of demographic and economic factors along with clinical factors, such as the use of other substances (i.e., cocaine), may provide strategies for enhancing retention in MMT. Improvements in retention are essential to reduce the occurrence of repeated treatment episodes and improve the overall clinical outcomes of these patients.
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Submitted: October 29, 2014 Revised: March 30, 2015 Accepted: April 1, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 906-917)
Accession Number: 2015-27693-001
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Record: 44- Premeditation moderates the relation between sensation seeking and risky substance use among young adults. McCabe, Connor J.; Louie, Kristine A.; King, Kevin M.; Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015 pp. 753-765. Publisher: American Psychological Association; [Journal Article] Abstract: Young adulthood is a peak period for externalizing behaviors such as substance abuse and antisocial conduct. Evidence from developmental neuroscience suggests that externalizing conduct within this time period may be associated with a 'developmental asymmetry' characterized by an early peak in sensation seeking combined with a relatively immature impulse control system. Trait measures of impulsivity—sensation seeking and premeditation—are psychological manifestations of these respective systems, and multiple prior studies suggest that high sensation seeking and low premeditation independently confer risk for distinct forms of externalizing behaviors. The goal of the present study was to test this developmental asymmetry hypothesis, examining whether trait premeditation moderates the effect of sensation seeking on substance use and problems, aggression, and rule-breaking behavior. Using a cross-sectional sample of college-enrolled adults (n = 491), we applied zero-inflated modeling strategies to examine the likelihood and level of risky externalizing behaviors. Results indicated that lower premeditation enhanced the effect of higher sensation seeking on higher levels of positive and negative alcohol consequences, more frequent drug use, and more problematic drug use, but was unrelated to individual differences in antisocial behaviors. Our findings indicate that the developmental asymmetry between sensation seeking and a lack of premeditation is a risk factor for individual differences in problematic substance use among young adults, and may be less applicable for antisocial behaviors among high functioning individuals. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Premeditation Moderates the Relation Between Sensation Seeking and Risky Substance Use Among Young Adults
By: Connor J. McCabe
Department of Psychology, University of Washington;
Kristine A. Louie
Department of Psychology, University of Washington
Kevin M. King
Department of Psychology, University of Washington
Acknowledgement:
Late adolescence and young adulthood is a developmental period characterized by the highest rates of externalizing behavior relative to any other period. Of those between 18 and 25 years of age, for instance, 40% are classified as binge drinkers (drinking 5 or more drinks in a single episode at least monthly), and 21% report current illicit drug use (U.S. Department of Health and Human Services, 2011). Aggression and delinquency—as well as more extreme forms of antisocial conduct such as violent criminal activity (Snyder, 2012)—also peak in adolescence and remain high in young adulthood (Loeber & Hay, 1997). Moreover, these are thought to presage more extreme antisocial behaviors and may serve as a marker for life-course-persistent antisocial conduct (Burt, Donnellan, Iacono, & McGue, 2011; Moffitt, 1993). Indeed, many—if not most—externalizing behaviors at all levels reach peak population prevalence between the ages of 16 and 25 (Steinberg, 2013).
Peaks in externalizing behavior in late adolescence and young adulthood may be explained by differential development of reward and control systems. Neurological studies suggest that reward sensitivity develops in a curvilinear pattern across puberty and young adulthood, generally peaking in midadolescence and declining through young adulthood (Galvan et al., 2006; Van Leijenhorst et al., 2010). Concurrently, structural magnetic resonance imaging (MRI) studies investigating the development of the prefrontal cortex (responsible for cognitive control over impulses) have shown that full maturation of this area follows a linear and protracted course through the third decade of life (Giedd, 2004; Gogtay et al., 2004; Paus, 2005; Somerville, Jones, & Casey, 2010). The disparate courses of development of these two systems produce a developmental asymmetry, and this asymmetry has been forwarded as an explanation for the high rates of risk behavior during adolescence (Casey, Jones, & Somerville, 2011; Steinberg, 2010). Specifically, adolescents may be particularly vulnerable to problematic engagement in externalizing behaviors because they have a higher propensity toward reward-driven behavior while their capacity to control such behavior is relatively immature.
Similar to neurological studies, studies using trait and behavioral measures suggest that adolescence reflects a time of increased sensation seeking and slowly developing impulse control. Increases in reward sensitivity are thought to increase trait sensation seeking, defined as the propensity to actively seek out novelty and excitement regardless of associated risks (Steinberg, 2013; Steinberg et al., 2008). Survey and behavioral measures of sensation seeking have shown similar patterns of development across the life span, peaking in midadolescence and generally declining after age 20 (Harden & Tucker-Drob, 2011; Romer, Duckworth, Sznitman, & Park, 2010; Steinberg et al., 2008). Evidence from cross-sectional and longitudinal studies suggest a similar course of development in survey and behavioral measures of impulse control, showing a linear increase across adolescence and young adulthood (Galvan, Hare, Voss, Glover, & Casey, 2007; Harden & Tucker-Drob, 2011; Romer & Hennessy, 2007; Steinberg, 2010).
However, it is also the case that a substantial proportion of individuals within this age range tend to abstain from externalizing behavior altogether (Romer, 2010), and that much externalizing behavior during this period may be driven by only a minority of individuals. For instance, National Household Survey (NHS) data report that among 12- to 20-year-olds, approximately 66% of drunk driving, 72% of criminal arrests, and 87% of all drug-related health problems were accounted for by only 18% of youths sampled (Romer, 2003; Biglan, Brennan, Foster, & Holder, 2004). This suggests that although adolescents and young adults do engage in problem behaviors more frequently on average, these figures may not reflect unilateral shifts in externalizing conduct, and a critical step is to translate the predictions of the developmental asymmetry model into predications about individual differences. Specifically, this model implies that the individuals who are highest on sensation seeking and lowest on impulse control will exhibit the greatest level of multiple externalizing behaviors. That is, regardless of developmental level, asymmetry between systems may predict high risk behaviors. However, to our knowledge, few studies have tested this hypothesis.
Sensation Seeking and Risk BehaviorSensation seeking has been a consistent predictor of externalizing behaviors in adolescent and young adult samples (Whiteside & Lynam, 2001, 2009; Zuckerman, 1979). Those high on sensation seeking may pursue risky activities as a result of a hedonic drive toward novel and rewarding activities, and measures of sensation seeking within this developmental period are indeed associated with engagement in multiple externalizing behaviors (Zuckerman & Kuhlman, 2000). Among young adults, sensation seeking is associated with greater drinking frequency (Coskunpinar, Dir, & Cyders, 2013; Stautz & Cooper, 2013), higher rates of drug use, aggression, and other forms of risky externalizing behaviors (see Roberti, 2004 for a review).
Although sensation seeking may reflect a broad propensity toward involvement in risky behavior, it is less clear whether sensation seeking confers direct vulnerability for problematic levels of risk behavior. For example, prior research has frequently demonstrated that there are often different predictors of substance use versus substance related problems (King, Karyadi, Luk, & Patock-Peckham, 2011; Simons, 2003; Stice, Barrera, & Chassin, 1998), particularly among young adults. Separately, relations between sensation seeking and problem behaviors may be confounded by a failure to disaggregate sensation seeking from other traits that confer risk (Whiteside & Lynam, 2009). For instance, trait measures of sensation seeking (such as the Zuckerman Sensation-Seeking Scale; Zuckerman et al., 1993) have frequently included items that reflect both a tendency toward novelty as well as a propensity toward acting on impulse, yet sensation seeking may reflect only one of a number of distinct, weakly correlated definitions of impulsivity (Cyders et al., 2007; Smith et al., 2007; Whiteside & Lynam, 2009). When controlling for these distinct factors in more recent studies, sensation seeking does independently predict risk behaviors such as the frequency of alcohol and drug use, count of sexual partners, and gambling frequency—but less consistently predicts problem levels of these activities per se (Cyders, Flory, Rainer, & Smith, 2009; Hawkins, Catalano, & Miller, 1992; Miller, Flory, Lynam, & Leukefeld, 2003; Quinn & Harden, 2013; Smith et al., 2007). This notion is supported by research that has observed no association between sensation seeking and problem behaviors above and beyond separate forms of trait impulsivity, such as urgency, lack of perseverance, and a lack of premeditation (Verdejo-García, Bechara, Recknor, & Pérez-García, 2007). Relatedly, in more recent meta analyses, sensation seeking was a moderate predictor of alcohol use (r values = 0.27 and 0.28), but was less strongly related to problem use (r values = 0.17 and .24; Coskunpinar et al., 2013; Stautz & Cooper, 2013). Whiteside and Lynam (2009) have further noted that sensation seeking may be associated with alcohol-related use and problems in adolescents (Bates & Labouvie, 1995; Wood, Cochran, Pfefferbaum, & Arneklev, 1995) but not in older adults (Lejoyeux, Feuché, Loi, Solomon, & Adès, 1998; Virkkunen et al., 1994), which may imply a potential role for psychological maturity to buffer the risk of sensation seeking among adults relative to younger populations. Taken together, these observations suggest that although a relation between sensation seeking and problem externalizing behaviors may exist, the nature of this relation may be moderated by separate contextual and psychological risk factors.
Impulse Control as a ModeratorOne potential moderator implied by the developmental asymmetry model—and a frequent predictor of risk behavior consequences—is trait impulse control. Impulse control has frequently been operationalized as the ability to think before acting and plan ahead (Whiteside & Lynam, 2001, 2009; Wills, Ainette, Stoolmiller, Gibbons, & Shinar, 2008), and is commonly referred to as planning, premeditation, or “good self-control,” with similar or identical items used as indicators of these parallel constructs (King, Patock-Peckham, Dager, Thimm, & Gates, 2014; Sharma, Markon, & Clark, 2014). A wide body of personality literature suggests a direct and inverse relation between impulse control and problem behaviors. For instance, low impulse control predicts higher rates of externalizing and conduct problems (Luengo, Carrillo-de-la-Peña, Otero, & Romero, 1994; Monahan, Steinberg, Cauffman, & Mulvey, 2009; Whiteside & Lynam, 2009); alcohol problems and heavy drinking (Coskunpinar et al., 2013; Stautz & Cooper, 2013); and drug use (Verdejo-García, Lawrence, & Clark, 2008), among others. These relations further persist above and beyond other known dispositional risk factors, such as sensation seeking, perseverance, and emotion-based impulsivity (i.e., urgency; Smith et al., 2007; Whiteside & Lynam, 2009).
More recent evidence suggests that impulse control (or a lack thereof) can also buffer (or enhance) the effects of other known risk factors for externalizing problems, though the construct has frequently been quantified heterogeneously. For instance, Wills and colleagues (2008) operationalized “good self-control” as a combined measure of planning and problem solving, and found that high levels of this trait reduced the effect of risk factors such as peer use and family events on frequency of cigarette, alcohol, and marijuana use in adolescents. Related findings have been observed in a daily diary study of college students (Neal & Carey, 2007), in which higher scores on the Eysenck Impulsiveness Scale (Eysenck, Pearson, Easting, & Allsopp, 1985)—a broad measure of acting without forethought and making hasty decisions—enhanced the relation between daily intoxication and the likelihood of experiencing consequences as a result of drinking. In a more recent study examining premeditation—or the tendency to think before acting—as a moderator, higher levels of the trait buffered the effect of depressive symptoms in predicting levels of alcohol problems, and enhanced this relation at low levels of premeditation (King et al., 2011).
Although numerous prior studies have tested the unique effects of impulse control and sensation seeking in the prediction of risk behaviors (e.g., Malmberg et al., 2010; Quinn & Harden, 2013; Roberts, Peters, Adams, Lynam, & Milich, 2014), few studies to date have tested interactions between these traits in predicting risk behaviors, and these studies have not reported evidence for the developmental asymmetry proposed in the present study (i.e., high sensation seeking and poor impulse control). For instance, in assessing levels of risky sexual behaviors, impulsive decision-making—a measure that encompasses aspects of both negative urgency and planning—was less strongly associated with sexual activity while intoxicated at higher levels of sensation seeking, suggesting that being high on either trait is risky, though being high on both traits predicts no greater risk (Charnigo et al., 2013). A separate study using a measure of self-control that included items measuring “breaking habits, resisting temptation, and keeping good self-discipline” (Tangney, Baumeister, & Boone, 2004, p. 275) found that good self-control had a buffering effect on unprotected sex and with a monogamous partner, as well as on alcohol problems among heavy drinkers, at lower levels of sensation seeking, but this study did not report whether the co-occurrence of poor self-control and high sensation seeking conversely predicted higher risk (Quinn & Fromme, 2010). By using measures of sensation seeking and impulse control that are clearer analogues of reward and control systems proposed by the developmental asymmetry model that also have well-established psychometric properties (e.g., Whiteside & Lynam, 2001), we aim to test whether the co-occurrence of high sensation seeking and poor impulse control characterizes synergistic risk for externalizing behaviors across multiple forms of externalizing conduct.
In the present study, we examined the moderating role of impulse control—operationalized in the present study as premeditation—on sensation seeking in predicting indicators of high risk substance use and delinquent behavior in a cross-sectional cohort of college-age young adults. Based on prior studies of known trait predictors of externalizing behavior, we expected that sensation seeking would likely predict externalizing behavior engagement (Magid & Colder, 2007), and a lack of premeditation would be associated with both engagement and more problematic levels of such behaviors (Smith et al., 2007; Stautz & Cooper, 2013). However, a lack of premeditation may additionally reflect a deficit in regulating reward drive, and may further have an enhancing effect on sensation seeking in predicting both frequency and problem levels of externalizing behavior. As such, we hypothesized that the co-occurrence of risk levels of these traits—that is, high sensation seeking and a lack of premeditation—would further characterize those with the most problematic levels of externalizing behavior.
Method Participants
Participants (n = 491) were undergraduate students in Psychology at the University of Washington who received course credit for survey participation. Participants completed the study in a single in person computer assisted interview session. A total of 34 participants were removed from antisocial behavior analyses due to missing data in criterion variables. Excluded participants did not significantly differ from retained participants in age (b = −.37, p = .08), gender, χ2(1 df) = .55, p = .46, or Asian American versus non-Asian American ethnicity, χ2(1 df) = .31, p = .57. A total of 56.6% of the participants were female. Approximately 55% were of Caucasian ethnicity, 33% of Asian or Pacific Islander ethnicity, and the remaining 12% reported being of other ethnicities. Participant age ranged from 18 to 24 with a median age of 19; 86% of participants were between 18 and 20.
Measures
Covariates
Gender, Asian/Asian American ethnicity, and age and were entered into the models as covariates to control for potential demographic differences in risk behavior outcomes. Gender was coded 0 for females and 1 for males. Because of the relatively high proportion of Asian American participants in our sample, and the lower mean prevalence rates of alcohol and drug use among Asian American populations, we included Asian American ethnicity as a covariate, coded as 1 = Asian/Pacific Islander ethnicity, and 0 for all other ethnicities. Although prevalence rates of substance use and risk behavior may be similarly lower for other minority groups, only a small proportion of participants reported minority status (e.g., 1.8% of the sample were African American, 4.9% were Hispanic/Latino) and meaningful comparisons for these groups could not be made. Current age was measured with a single self-report item.
Sensation seeking and premeditation
Premeditation (11 items) and sensation seeking (12 items) were measured via self-report from the Urgency, Premeditation, Perseverance and Sensation Seeking (UPPS) Impulsive Behavior Scale (Whiteside & Lynam, 2001). Participants were instructed to rate how well each statement described them. Response options for all facets were on a 5-point Likert scale ranging from not at all to very much. Sample premeditation items included “My thinking is usually careful and purposeful” and “I don’t like to start a project until I know exactly how to proceed.” Sample sensation seeking items included “I like sports and games in which you have to choose your next move very quickly” and “I would enjoy fast driving.” Facet scores were computed by taking the mean of the items for that facet. Internal consistency coefficients were high for both premeditation and sensation seeking (α = .87, α = .91, respectively). We coded premeditation such that higher scores reflected higher levels of premeditation, and higher sensation seeking scores reflected higher sensation seeking. Consistent with previous findings (e.g., Cyders et al., 2009), sensation seeking and premeditation were only modestly correlated in the present sample (r = −.26).
Substance Use
Alcohol use
Participants self-reported their frequency and quantity of alcohol consumption in the past year with four items. Frequency was assessed using two items (one for beer/wine and one for hard liquor) with responses ranging from never to every day. Quantity was assessed with two items (one for beer/wine, one for hard liquor) asking how much the participant drank in the past year on a “typical” occasion, ranging from 1 to 9 or more drinks per occasion. A single alcohol use variable was computed as the sum of the products of the beer/wine quantity*frequency and the hard liquor quantity*frequency variables.
Alcohol consequences
We assessed the consequences of alcohol use in two ways. First, to assess the immediate risk posed by alcohol use, participants self-reported on 39 negative consequences related to alcohol use in the past year. A total of 27 items were from the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher, 1992), including items such as “Have you ever been arrested for drunk driving, driving while intoxicated, or driving under the influence of alcohol?,” “Have you ever felt like you needed a drink just after you’d gotten up?,” and “Have you ever had “the shakes” after stopping or cutting down on drinking?,” reflecting traditional symptoms of alcohol abuse and dependence. Twelve negative consequences were taken from Mallett, Bachrach, and Turrisi (2008) to reflect alcohol consequences that may be common to young adults, but may or may not be traditionally represented in indices of alcohol disorders. These items include “Have you ever urinated on yourself because of your drinking?,” “Have you ever been embarrassed socially because of your drinking?,” or “Have you ever lost personal items because of your drinking?”
We also measured positive alcohol-related consequences, to assess for potentially reinforcing effects of alcohol use that might presage later escalations in drinking (Logan, Henry, Vaughn, Luk, & King, 2012; Park, Kim, & Sori, 2013). We used 14 items from the Positive Drinking Consequences Questionnaire (PDCQ; Corbin, Morean, & Benedict, 2008), and included items such as “Have you ever stood up for a friend or confronted someone who was in the wrong while drinking?” and “Have you ever felt especially confident that other people found you attractive while you were drinking?”
For both scales, 10 response options ranged from never or not in the past year to 1 time in the past year to 40 or more times in the past year. We computed pseudocounts for negative and positive alcohol consequences, reflecting the sum of past year perceived frequency of these alcohol-related consequences. We computed coefficient alphas as a measure of consistency among items for each of these scales in order to assess whether items for each reflect an underlying construct of alcohol dyscontrol (e.g., Hurlbut & Sher, 1992; Read, Merrill, Kahler, & Strong, 2007); alphas for these scales were .94 and .92, respectively.
Drug use
Illicit drug use was self-reported using 11 items measuring the frequency of using marijuana, inhalants, cocaine, stimulants, club drugs, hallucinogens, opiates, and steroids within the past year. The seven response options for past year consumption ranged from not at all to everyday. Total past year drug use was computed as a sum of drug use frequency across all the items. Reliability for this measure was .62. Because 31.4% of all participants reported marijuana use, and few reported use of illicit substances other than marijuana (17.1%), we also examined the single-item frequency of past year marijuana use as a separate risk outcome.
Drug consequences
Participants self-reported the frequency of negative drug consequences experienced within the past year. A total of 39 items assessed the number of times a consequence occurred in the past year, with 10 categories ranging from never or not in the past year to1 time in the past year to 40 or more times in the past year. Items were derived from the YAAPST (Hurlbut & Sher, 1992) and Mallett et al. (2008), and were modified to apply to drug use outcomes. Sample items included “Have you gotten into physical fights when using drugs?” and “Have you ever been arrested for driving under the influence of drugs (besides alcohol)?” Similar to the alcohol consequences variable, we computed a pseudocount variable by summing across all 39 consequence items. Reliability for this scale was .88.
Antisocial Behavior
Aggression and rule-breaking behavior
Aggression and rule-breaking behaviors were measured using the Achenbach Adult Self Report (ASR; Achenbach & Rescorla, 2003). These subscales consisted of 15 and 14 self-reported items, respectively, ranging on a 3-point scale from not true to very true or often true. Sample aggressive behavior items included “I get along badly with my family” and “I get in many fights.” Sample rule-breaking behavior items included “I hang around people who get in trouble” and “my behavior is irresponsible.” Subscale scores were computed as means, and were transformed into T scores based on national norms (Achenbach & Rescorla, 2003). Reliability for these scales were .79 and .77, respectively.
Results Analytic Strategy
Our outcome measures of annual drug and alcohol use frequency, drug and alcohol consequences, and externalizing behavior scores were highly overdispersed, with a substantial proportion of the sample reporting no occurrence of many risk-taking behaviors and few reporting at or near the maxima of these outcomes (see Table 1 for summary). As such, we explored a variety of analytic methods for modeling nonnormally distributed data. Although sum scores of ordinal data (such as self-report frequency of past year alcohol-related consequences collected on an ordered categorical scale) do not reflect true counts, their distributions behaved very much like zero-inflated count distributions, in that there were excessive zeroes, many participants with low scores, and very high skew with very few high-scoring participants. Although modeling approaches for treating ordinal data as counts exist for single-item indicators (McGinley, Curran, & Hedekr, n.d.), these methods have not yet been extended to sums of ordinal items (McGinley, personal communication). Thus, we modeled these data treating outcomes as “pseudocount” data (e.g., counts of past year alcohol and drug use and consequences), analyzed using zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and negative binomial hurdle modeling (NBH) for highly zero-inflated count data (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013; Bandyopadhyay, DeSantis, Korte, & Brady, 2011). These zero-inflated strategies are used to analyze outcome variables in two separate regression models: a logistic regression predicting the logged-odds of a binary zero versus nonzero value of the outcome, and a separate model predicting counts among those reporting nonzero values of the outcome variable. In the present context, this allowed us to predict the relative likelihood of experiencing any risk behavior compared with experiencing none at all, and separately, the “count” of these outcomes among those engaging in these behaviors. We tested all substance use and substance-related problem outcomes assuming a normal (Gaussian) distribution as a baseline model, then specified models using ZIP, ZINB, and NBH. Final model selection was determined by comparing Akaike information criteria (AIC) and Bayesian information criteria (BIC), and were tested formally using likelihood ratio tests for nested models and Vuong tests for non-nested models (Vuong, 1989). Models with lower information criteria were selected when likelihood tests were nonsignificant. Based on these results, NBH was selected for models of past year alcohol, drug, and marijuana use, and ZINB was used to model negative and positive drinking consequences as well as drug use consequences. For highly skewed continuous measures of aggression and rule-breaking behavior, semicontinuous (or two-part) regression was used for analysis (Gottard, Stanghellini, & Capobianco, 2013), and coefficients produced by this modeling strategy are interpreted similarly to NBH. Comparisons of model fit using likelihood ratio and Vuong tests for each outcome are provided in Table 2.
Sample Descriptive Statistics
Model Selection Criteria
We included age, Asian American ethnicity, and gender as covariates to control for expected group differences in problem risk behaviors. We probed significant interactions using the pick-a-point approach (Aiken & West, 1991). Probing interactions between sensation seeking and premeditation were conducted at high (+1 SD), mean, and low (−1 SD) levels of premeditation. All main predictors within the models were centered at zero to simplify the interpretation of regression coefficients (Cohen, Cohen, West, & Aiken, 2003). Effect sizes are reported as odds ratios (OR) in the likelihood portions of our models and rate ratios (RR) in the count portions, which refer to factor increases in the odds of a dichotomous outcome, and factor increases in the predicted count outcome resulting from single-unit increases in predictor values, respectively (Atkins et al., 2013). Descriptive data analyses were performed using Statistical Packages for the Social Sciences (SPSS) 20.0, and all other analyses were performed using R (R Development Core Team, 2014). Zero-inflated count models were estimated using package “pscl” (Zeileis, Kleiber, & Jackman, 2008), and semicontinuous outcomes using “mhurdle” (Carlevaro, Croissant, & Hoareau, 2012).
Premeditation as a Moderator of Sensation Seeking on Alcohol Use and Consequences
We first examined the effects of sensation seeking, premeditation, and their interaction on the quantity and frequency of past year alcohol use, negative alcohol consequences, and positive alcohol consequences. Results from these models are reported below and summarized in Table 3.
Sensation Seeking, Premeditation, and Drinking Behavior
Higher sensation seeking increased the likelihood of past year alcohol use, while premeditation decreased in the likelihood of use. In the count portion of our model, premeditation was associated with lower levels of use among alcohol-using participants. No other effects were significant.
Similar to alcohol use, sensation seeking increased the likelihood of experiencing negative alcohol consequences, and premeditation decreased the likelihood of consequences. The effects on positive alcohol consequences were similar: higher sensation seeking was associated with a higher likelihood of positive consequences, and higher premeditation decreased the likelihood.
Moreover, we observed a significant interaction between sensation seeking and premeditation predicting the counts of both negative and positive alcohol consequences. At mean levels of sensation seeking, premeditation was associated with fewer positive and negative consequences in the count portion of our model. Further, sensation seeking was associated with alcohol consequences when premeditation was low, but was unrelated to consequences when premeditation was at mean or high levels. When premeditation was low, a unit increase in sensation seeking was associated with a nearly significant 15% increase in negative consequences, RR = 1.15, p = .057, 95% CI [1.00, 1.34], and a 12% increase in positive consequences, RR = 1.12, p = .036, 95% CI [1.01, 1.25]. Figure 1 illustrates these effects.
Figure 1. The synergistic effects of sensation seeking and premeditation on substance use outcomes. Lines represent relations between sensation seeking and alcohol consequences at low (−1 SD) and high (+1 SD) levels of premeditation. Shaded regions represent simulated 95% confidence intervals. * p ≤ .05. ** p ≤ .01. *** p ≤ .001.
Premeditation as a Moderator of Sensation Seeking on Drug Use and Consequences
We next tested premeditation as a moderator of the effects of sensation seeking on drug use and consequences. Results are reported below and in Table 4.
Sensation Seeking, Premeditation, and Drug Behavior
Similar to the effects of alcohol outcomes reported above, sensation seeking was associated with a higher likelihood of drug use, while premeditation was associated with a lower likelihood. Results for the likelihood of marijuana use were similar for both sensation seeking and premeditation. Although neither sensation seeking nor premeditation predicted the level of drug use alone, we found evidence of an interaction. When premeditation was low, a unit increase in sensation seeking was associated with a 69% increase in the predicted count of past year drug use, RR = 1.69, p = .002, 95% CI [1.21, 2.38], but predicted no change in drug use when premeditation was at mean and high levels. These effects are shown in Figure 1. We did not observe this interaction when marijuana was a sole outcome.
For our drug consequences outcome, results largely mirrored those reported for drug use. Higher sensation seeking was associated with a higher likelihood of consequences, and premeditation was associated with a lower likelihood, and we observed an interaction predicting levels of drug consequences. At mean and high levels of premeditation, sensation seeking was unrelated to the level of drug consequences; however, when premeditation was low, sensation seeking trended toward an association with drug consequences, predicting a 28% increase in the count for every unit change, RR = 1.28, p = .058, 95% CI [0.99, 1.65], but this pattern was not significant—and in fact trended toward the reverse—at higher levels of premeditation, RR = 0.75, p = .096, 95% CI [0.54, 1.05]. That is, sensation seeking trended toward being protective against higher drug consequences when premeditation was high, but was a risk factor when premeditation was low. Figure 1 illustrates these effects.
Premeditation as a Moderator of Aggression and Rule-Breaking Behaviors
Finally, we tested the effects of sensation seeking, premeditation, and their interaction on aggression and rule-breaking behavior (see Table 5 for summary). Unlike our substance use outcomes, we found no main effects or interactions for aggression. Sensation seeking was a risk factor for higher levels of rule-breaking behavior, and similarly predicted a greater likelihood of reporting any rule-breaking behavior. Premeditation was unrelated to rule-breaking behavior among those reporting any rule-breaking behavior, but was protective against the likelihood of exhibiting such behavior. The interaction between sensation seeking and premeditation was not significant.
Sensation Seeking, Premeditation, and Antisocial Behavior
DiscussionThe goal of the present study was to test whether the interaction between sensation seeking and premeditation was associated with externalizing behaviors in young adulthood. Our main effect findings yielded consistent results: higher sensation seeking characterized individuals who were more likely to be alcohol and drug users within the past year, while premeditation characterized those who were less likely to use alcohol and drugs and those who drank less among alcohol-initiated participants. Moreover, our results provided moderate support for the developmental asymmetry hypothesis among substance use outcomes: a combination of high sensation seeking and a lack of premeditation characterized those with the highest rates of drinking consequences, drug use, and drug consequences, suggesting that an asymmetry between high sensation seeking and a lack of premeditation may be a critical risk indicator for heightened substance abuse among young adults.
Although previous findings have reported mixed or weak relations between sensation seeking and problematic drinking (Coskunpinar et al., 2013; Smith et al., 2007; Stautz & Cooper, 2013), our findings clarified and extended these results: the relation between sensation seeking and problem levels of drinking may be stronger among individuals who also lack premeditation. Importantly, the effects were similar whether we examined negative alcohol consequences, which likely reflect risky behavior that directly results from alcohol use, or positive alcohol-related consequences, which may serve as a marker of those at higher risk for heavier alcohol involvement (Park et al., 2013). The present findings were the first to provide direct evidence for these synergistic associations. One prior study failed to detect this interaction (Quinn & Fromme, 2010). It may be that using modeling strategies that address zero inflation and overdispersion in outcomes provided increased sensitivity to detect these effects. Second, we used trait predictors designed to measure orthogonal constructs of impulsivity (Whiteside & Lynam, 2009) that may be more appropriate psychological analogues to the neurobiological dual systems model proposed by Steinberg (2010). Third, we did not covary for drinking quantity/frequency to avoid the concern of collinearity between drinking levels and drinking consequences. Although some research suggested that sensation seeking increased the risk of substance related problems merely by increasing substance use frequency (Magid & Colder, 2007; Quinn & Fromme, 2010), our previous work suggested that high sensation seekers experience more consequences for a given level of alcohol use (King et al., 2011). Future research should attempt to disaggregate the mediating/moderating role of alcohol use more directly.
The present study was also, to our knowledge, the first to test this interaction predicting drug use. Multiple prior studies have reported relations between multidimensional constructs of trait impulsivity and multiple forms of drug use, including heavy ecstasy use (Parrott, Milani, Parmar, & Turner, 2001), cocaine use (Coffey, Gudleski, Saladin, & Brady, 2003; Moeller et al., 2002), and heroin use (Kirby, Petry, & Bickel, 1999; Madden, Petry, Badger, & Bickel, 1997), while sensation seeking has been previously associated with stimulant use (Leland & Paulus, 2005) and heroin dependence (Dissabandara et al., 2014). The present findings add to this literature: risk levels of both traits predicted a higher probability of drug use engagement, and their co-occurrence was synergistically associated with more polydrug use and problems. Moreover, although marijuana use was the single most commonly used illicit substance in our sample and both sensation seeking and premeditation predicted higher likelihoods of having used marijuana, we did not find evidence of significant main or interactive effects among marijuana-using college students. Although some previous findings have observed main effects of trait impulsivity on problematic marijuana use (e.g., Day, Metrik, Spillane, & Kahler, 2013), still others have failed to observe these relations (Butler & Montgomery, 2004; Dvorak & Day, 2014; Simons, Neal, & Gaher, 2006; Verdejo-García et al., 2008). It is possible that these and the present results may be confounded by distinctions in motivations toward marijuana use versus other illicit substances. Although young adults may use certain substances primarily to seek stimulation (e.g., MDMA; Peters, Kok, & Abraham, 2008), a proportion of marijuana users may separately use to cope with negative emotional states such as anxiety (Bonn-Miller, Zvolensky, & Bernstein, 2007); thus, the relation between marijuana use, sensation seeking, and premeditation may be particularly attenuated by separate predictors of use such as coping motives, and we encourage future studies to address these relations. Moreover, we encourage others to address transactions and interactions between sensation seeking and premeditation predicting patterns of drug use in a longitudinal sample; some prior research suggests that impulsivity may be an earlier marker for drug abuse while sensation seeking a product of abuse (Ersche, Turton, Pradhan, Bullmore, & Robbins, 2010), yet the precise nature of these relations has yet to be investigated.
The present dual systems framework hypothesizes that the developmental asymmetry explains the relative peak in criminal and antisocial conduct such as robbery, burglary, and forcible rape among young adults (Steinberg, 2013), among other risky behaviors. Although relations between aggressive behavior and poor impulse control and sensation seeking have been reported previously (Monahan et al., 2009; Wilson & Scarpa, 2011), we did not observe a synergistic interaction between these traits. There may be two reasons why we did not observe this result. First, the present data was drawn from a community sample of college-enrolled young adults, with very few respondents reporting clinically relevant levels of aggression (3.1%) or rule-breaking behavior (7.7%). Future research investigating this developmental asymmetry in more vulnerable populations, such as among incarcerated samples, may find evidence among “riskier” young adults exhibiting higher rates of antisocial conduct. Second, our measures of self-reported aggression and rule-breaking behavior may not have been sufficiently sensitive constructs to properly measure “antisocial” behavior in this population. Although these measures are considered ecologically valid (Achenbach et al., 2003), they may not capture the range of subthreshold antisocial behavior that may be observed in non- or preclinical populations. Relatedly, measures separate from antisocial conduct that are more relevant to lower-level risky behavior in college may yield more promising results, such as risky sexual conduct (Charnigo et al., 2013) and risky driving (Pharo, Sim, Graham, Gross, & Hayne, 2011).
Our findings indicate that sensation seeking increases the likelihood of engaging in multiple forms of externalizing behaviors and is also a conditioned risk indicator of externalizing problems (i.e., is unrelated directly to levels of substance use unless coupled with a lack of premeditation). These findings are consistent with a more nuanced perspective of the trait (e.g., Ravert et al., 2013): sensation seeking may confer risk for engaging in novel (and sometimes risky) behaviors, but may not be a direct indicator of risk for consistent and problematic engagement in itself. Moreover, some prior research has observed positive developmental outcomes among sensation-seekers, such as higher IQ (Bayard, Raffard, & Gely-Nargeot, 2011; Raine, Reynolds, Venables, & Mednick, 2002), psychological well-being (Ravert et al., 2013), and age-related improvements in the ability to delay gratification (Romer, 2010), suggesting that a propensity toward novelty may be adaptive and yield positive results in certain environments. We found marginal evidence of this in the present study among those experiencing drug consequences: the nature of the interaction between sensation seeking and premeditation was such that the effect of sensation seeking on drug consequences reversed in its direction depending on level of premeditation, implying that high levels of these traits might constitute protection in the context of problematic drug use. It may be the case that separate contextual factors drive sensation-seekers toward more adaptive forms of reward-driven behaviors (Romer & Hennessy, 2007); this interpretation warrants caution given that this effect was not the focus of the present analyses, though we encourage this question be addressed directly in future research.
Our results reflect an estimate of the associations between developmental asymmetry and externalizing behaviors during young adulthood. It may be that this interaction differs across development. Several factors highlight the importance of considering development in predicting externalizing behaviors from asymmetry. First, although longitudinal reports indicated that sensation seeking remains high through the mid-twenties (Harden & Tucker-Drob, 2011), studies suggested that sensation seeking peaks earlier in development, specifically during midadolescence (Romer et al., 2010; Steinberg et al., 2008). Given the protracted and linear development of impulse control, asymmetry between these two systems might therefore be greatest on average during this developmental period. Second, engaging in substance use earlier in development is less normative than drinking at older ages and may reflect greater propensity toward delinquency than college-age drinking. Evidence has indicated that earlier initiation and problematic use within adolescence were markers for substance disorder throughout the life span (King & Chassin, 2007; McGue & Iacono, 2005), and we might expect that asymmetry within this period is a particularly critical indicator of delinquency both within the period and in later life. Third, although we did not find evidence of an association between developmental asymmetry and antisocial behavior among young adults, developmental asymmetry may capture antisocial behavior that is limited to adolescence (Moffitt, 1993) and largely desists by young adulthood (Steinberg, 2013). Taken together, the effect of the developmental asymmetry on multiple forms of delinquency may be stronger during adolescence on average and may predict other period-specific externalizing behaviors, though translation of these population-level predictions to examining individual differences within adolescence remains unexplored.
The present study provides evidence that the dual systems framework does predict individual differences in problematic substance use among college-enrolled adults (Strang, Chein, & Steinberg, 2013). The present study has a number of strengths, including the application of advanced quantitative methods to appropriately specify models for low base-rate risk behavior and the application of psychometrically validated measures of trait impulsivity. The present study also has a number of limitations that should be considered. First, our results are cross-sectional, and precise causal inferences between trait impulsivity and risk behavior cannot be made. For instance, substance use may result in an increase in impulsiveness or sensation seeking over time (Ersche et al., 2011; Littlefield, Vergés, Wood, & Sher, 2012), and correlational data in either cross-sectional or longitudinal designs is not sufficient for ensuring causal precedence of impulsive traits. Second, as mentioned previously, these results represent findings among a community sample of college-enrolled young adults; two critical future directions include extending these findings to younger adolescent populations—among whom the developmental asymmetry is theoretically paramount in predicting externalizing and other risk behavior—as well as vulnerable populations such as clinical samples and incarcerated youth. Third, the present study sought to test the specific interaction between trait measures of sensation seeking and premeditation, but other dispositions toward impulsive behavior may explain additional variance in substance abuse outcomes, or may buffer or enhance the effects observed in the present study. For instance, positive and negative urgency—which reflect dispositions toward positive and negative emotion-based rash action—are critical and independent determinants of problem behaviors in themselves (Cyders & Smith, 2007) and may similarly interact with impulsive traits specified in the present study. Relatedly, we encourage future research to examine the developmental asymmetry model using behavioral measures of these constructs as well. Although support for direct overlap between laboratory-based behavioral measures and self-reported trait measures is modest, both methods of measuring these constructs have independently predicted externalizing behavior outcomes (Cyders & Coskunpinar, 2011; Sharma et al., 2014). Thus, examining interactions between reward sensitivity and impulse control measured using laboratory tasks may provide additional support for the developmental asymmetry model. Our findings underscore that dispositions toward impulsive behavior are critical in the assessment, prevention, and intervention of risk-taking behavior, and that examining the synergistic impact of these traits can provide additional insight in understanding individual differences in problem behaviors.
Footnotes 1 Negative and positive drinking consequences were highly correlated with alcohol use in the present sample (r values = 0.83 and 0.81, respectively). When use was included as a covariate, fitted probabilities for consequences were numerically 0 or 1 in the zero inflation portion due to perfect collinearity between use and consequences, and reliable estimates for predictors of interest could not be obtained.
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Submitted: August 18, 2014 Revised: February 17, 2015 Accepted: February 18, 2015
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Record: 45- Prevalence and correlates of cannabis use in an outpatient VA posttraumatic stress disorder clinic. Gentes, Emily L.; Schry, Amie R.; Hicks, Terrell A.; Clancy, Carolina P.; Collie, Claire F.; Kirby, Angela C.; Dennis, Michelle F.; Hertzberg, Michael A.; Beckham, Jean C.; Calhoun, Patrick S.; Psychology of Addictive Behaviors, Vol 30(3), May, 2016 pp. 415-421. Publisher: American Psychological Association; [Journal Article] Abstract: Recent research has documented high rates of comorbidity between cannabis use disorders and posttraumatic stress disorder (PTSD) in veterans. However, despite possible links between PTSD and cannabis use, relatively little is known about cannabis use in veterans who present for PTSD treatment, particularly among samples not diagnosed with a substance use disorder. This study examined the prevalence of cannabis use and the psychological and functional correlates of cannabis use among a large sample of veterans seeking treatment at a Veterans Affairs (VA) PTSD specialty clinic. Male veterans (N = 719) who presented at a VA specialty outpatient PTSD clinic completed measures of demographic variables, combat exposure, alcohol, cannabis and other drug use, and PTSD and depressive symptoms. The associations among demographic, psychological, and functional variables were estimated using logistic regressions. Overall, 14.6% of participants reported using cannabis in the past 6 months. After controlling for age, race, service era, and combat exposure, past 6-month cannabis use was associated with unmarried status, use of tobacco products, other drug use, hazardous alcohol use, PTSD severity, depressive symptom severity, and suicidality. The present findings show that cannabis use is quite prevalent among veterans seeking PTSD specialty treatment and is associated with poorer mental health and use of other substances. It may be possible to identify and treat individuals who use cannabis in specialty clinics (e.g., PTSD clinics) where they are likely to present for treatment of associated mental health issues. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Prevalence and Correlates of Cannabis Use in an Outpatient VA Posttraumatic Stress Disorder Clinic / BRIEF REPORT
By: Emily L. Gentes
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Durham VA Medical Center, Durham, North Carolina
Amie R. Schry
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Durham VA Medical Center, Durham, North Carolina
Terrell A. Hicks
Department of Psychiatry and Behavioral Sciences, Duke University
Carolina P. Clancy
Durham VA Medical Center, Durham, North Carolina
Claire F. Collie
Durham VA Medical Center, Durham, North Carolina
Angela C. Kirby
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Durham VA Medical Center
Michelle F. Dennis
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Department of Psychiatry and Behavioral Sciences, Duke University
Michael A. Hertzberg
Department of Psychiatry and Behavioral Sciences, Duke University, and Durham VA Medical Center
Jean C. Beckham
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Department of Psychiatry and Behavioral Sciences, Duke University
Patrick S. Calhoun
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Department of Psychiatry and Behavioral Sciences, Duke University;
Acknowledgement: Emily L. Gentes is now at Butler Hospital, Providence, Rhode Island.
This work was supported by the VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center and Durham VA Medical Center. Emily L. Gentes’s and Amie R. Schry’s contributions to this article were also supported by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment, and Jean C. Beckham’s contributions were also supported by a Research Career Scientist Award from the Clinical Science Research and Development Service of the VA Office of Research and Development. The Department of Veterans Affairs had no involvement in the study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the paper for publication. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the VA or the U.S. government or any of the institutions with which the authors are affiliated.
Cannabis is the most frequently used illicit substance in the United States (Substance Abuse & Mental Health Services Administration, 2014) and has been associated with a wide range of health issues, particularly related to cardiopulmonary and mental health (Goldman et al., 2010; Moussouttas, 2004). In particular, recent research has documented high rates of comorbidity between cannabis use disorders and posttraumatic stress disorder (PTSD; Agosti, Nunes, & Levin, 2002) across both civilian and veteran populations. Among U.S. adults, PTSD is associated with increased odds of cannabis use, even when adjusting for sociodemographic variables, alcohol use disorders, nicotine dependence, co-occurring anxiety and mood disorders, and trauma type frequency (Cougle, Bonn-Miller, Vujanovic, Zvolensky, & Hawkins, 2011). Among veterans, rates of PTSD are higher in those with a cannabis use disorder compared to those with other substance use disorders (Bonn-Miller, Harris, & Trafton, 2012).
The veteran population may be at particular risk for elevated rates of cannabis use, as well as its negative effects on physical and mental health because veterans tend to report higher rates of the medical and psychological problems associated with problematic cannabis use (Hoerster et al., 2012; Kessler et al., 2014). In particular, veterans who have been exposed to combat and have PTSD may use cannabis to cope with symptoms such as anxiety, insomnia, and depression (Boden, Babson, Vujanovic, Short, & Bonn-Miller, 2013) with attempts at self-medication resulting in high rates of cannabis use in this population. Overall, rates of cannabis use disorder within the Veterans Affairs (VA) Health Care System have increased more than 50% (from 0.66% to 1.05%) from 2002 to 2009 (Bonn-Miller et al., 2012). However, utilization of specialty treatments for substance use disorders has decreased among those with a cannabis use disorder (Bonn-Miller et al., 2012). It may therefore become important to identify individuals who seek treatment in other VA clinics and may also struggle with cannabis use. Despite possible links, relatively little is known about the prevalence of cannabis use and its demographic and psychiatric correlates in veterans who present for PTSD specialty treatment.
Furthermore, research examining the consequences of cannabis use among veterans has focused almost exclusively on individuals who meet diagnostic criteria for a cannabis use disorder. However, individuals may experience negative physical health, mental health, psychosocial, and legal effects resulting from cannabis use without meeting full criteria for cannabis use disorder (Calhoun, Malesky, Bosworth, & Beckham, 2005; Fergusson, Horwood, & Beautrais, 2003; Goldman et al., 2010; Moussouttas, 2004), highlighting the importance of studying cannabis use among a broader veteran population. One recent study found that 11.5% of veterans being referred from primary care for initial behavioral health assessment reported past-year cannabis use. Age, gender, other past-year drug use, presence of alcohol use disorders, smoking status, depressive disorders, PTSD, anxiety disorders, and psychotic symptoms were each found to independently predict veterans’ cannabis use over the past year. After adjusting for demographic variables (age, race, and gender), only other substance use including past-year drug use, alcohol use disorders, and cigarette use remained associated with past-year cannabis use (Goldman et al., 2010).
The purpose of the present study was to extend Goldman and colleagues’ (2010) findings by examining the prevalence of cannabis use and its psychological and functional correlates among a large sample of veterans seeking treatment at a VA PTSD specialty clinic.
Method Participants and Procedures
Archival data were analyzed from 719 male veterans who presented at a specialty outpatient PTSD clinic at a VA hospital in the southeastern United States. Patients presenting to this clinic completed a diagnostic evaluation to assess the presence and severity of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association [APA], 2000) PTSD symptoms. Participants completed all measures as part of their standard clinic evaluation. Participants who completed an evaluation between 1998 and 2008 were included in the current study. The study was determined by the institutional review board to be exempt from review because data were collected as part of standard clinic evaluation and did not include any identifying information. Demographic data are presented in Table 1. Only male veterans were included in the present study because only 23 females had available data in the sample, which limited the ability to examine possible gender differences.
Demographic, Substance Use, and Clinical Characteristics of Cannabis Users and Non-Users
Measures
As part of clinic procedures, demographic data including age, race, marital status (married vs. unmarried), employment status (employed vs. unemployed), and number of health problems experienced over the past year were collected. Additional self-report data were collected on difficulty controlling violent behavior in the past month (yes or no).
The presence of PTSD symptoms was assessed using the Clinician-Administered PTSD Scale (CAPS; Blake et al., 1995), a structured clinical interview that evaluates the frequency and intensity of the 17 symptoms of PTSD as defined in the DSM–IV (APA, 2000). Scores from the CAPS interview have been shown to demonstrate excellent reliability and validity within multiple trauma populations, and it is widely accepted as the gold standard for PTSD assessment (Weathers, Keane, & Davidson, 2001; Weathers, Ruscio, & Keane, 1999). A clinical psychologist supervised all evaluations. Interrater agreement among clinicians for PTSD diagnosis was excellent (κ = .92). The CAPS total score both overall and in each cluster (i.e., Cluster B reexperiencing symptoms, Cluster C avoidance symptoms, Cluster D hyperarousal symptoms) was computed by summing the frequency and intensity ratings for all items in each cluster. Clinicians also rated the global severity of the patient’s PTSD symptoms on the following 5-point scale: 0 (none), 1 (mild), 2 (moderate), 3 (severe), and 4 (extreme). Interrater reliability for global severity ratings was high (κ = .82).
Self-report data on cannabis and other drug use were collected through the use of a questionnaire that asked about the frequency of use of specific drugs (e.g., cannabis, amphetamines, cocaine, heroin) during the past 6 months. Response options for each drug included the following: no use, daily use, weekly use, use once every 2 weeks, use once every 3 weeks, use once every month, use once every 3 months, and use once every 6 months. There were no negative consequences directly attached to reporting substance use, although patients were instructed that all information collected during their evaluations would be made part of their medical record. Drug use self-reports have been demonstrated to be highly valid in veterans seeking help for PTSD (Calhoun et al., 2000).
The Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993) is a 10-item measure assessing three factors: alcohol consumption, alcohol dependence, and adverse consequences of alcohol use. The range of possible scores is 0–40, with higher scores indicating increased probability of an alcohol use disorder. The AUDIT has been found to have a high level of agreement with other measures of alcohol use disorders (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). The internal consistency (i.e., Cronbach’s alpha) of the AUDIT in the current sample was .92.
The Combat Exposure Scale (CES; Keane et al., 1989) was used to assess combat exposure. The CES is a widely used 7-item, Likert-type scale designed to measure wartime trauma exposure. The total score ranges from 0 to 41 and is a sum of weighted scores. Cronbach’s alpha of the CES in the current sample was .89.
The Beck Depression Inventory—II (BDI-II; Beck, Steer, & Brown, 1996) was used to assess current depression symptoms. The BDI-II is a 21-item measure with total scores ranging from 0 to 63, where higher scores are indicative of more severe depression symptoms. Suicidality was measured using an item from the BDI-II, which participants rated on a scale from 0 to 3, where higher scores are indicative of greater suicidality. Scores from the BDI-II have been shown to be valid and reliable (Beck et al., 1996). The internal consistency of the BDI-II in the current sample was excellent (α = .91).
The Quality of Life Inventory (QOLI; Frisch, 1994, 1998) includes 32 items that assess satisfaction across 16 important life areas including health, self-esteem, goals and values, money, work, play, learning, creativity, helping, love, friends, children, relatives, home, neighborhood, and community. Participants first rate the importance of each domain on a 3-point scale from 0 (not important) to 2 (extremely important). Then they rate their satisfaction on a 7-point scale from −3 (extremely dissatisfied) to 3 (extremely satisfied). Scores range from −6 to 6, with higher scores indicative of greater quality of life. The internal consistency (i.e., Cronbach’s alpha) of the QOLI in the current sample was .89.
Statistical Analyses
All variables were screened for outliers. Descriptive statistics were calculated to characterize demographic, substance use, and psychological attributes of participants. In order to facilitate interpretation of results from logistic regression, z scores were calculated for continuous variables including combat exposure (CES), hazardous alcohol use (AUDIT total score), PTSD symptom severity (CAPS total score, CAPS reexperiencing, CAPS avoidance, CAPS hyperarousal, CAPS clinician-rated global severity score), depression (BDI-II), suicidality (BDI-II item 9), and quality of life (QOLI).
Unadjusted and adjusted logistic regression analyses were used to examine the association between marijuana use and demographic, substance use, and psychological variables. No past 6-month cannabis use served as the reference category in each model. Adjusted models examined the association of marijuana use with each variable after adjusting for age, race, service era, and combat exposure.
Results Participant Characteristics
The majority of participants (n = 658, 91.5%) in the present sample met DSM–IV diagnostic criteria for PTSD at the time of assessment. One hundred five participants (14.6%) reported using cannabis at least once during the past 6 months. Among those who reported use of cannabis in the past 6 months, 27.6% reported daily use, 35.2% reported weekly use, 8.6% reported biweekly use, 3.8% reported use every 3 weeks, 9.5% reported monthly use, 3.8% reported use every 3 months, and 11.4% reported use every 6 months.
Predictors for Past Six Months Cannabis Use
Descriptive statistics are presented in Table 1. Bivariate analyses were used to examine the relationship between demographic, substance use, and psychological variables and cannabis use (see unadjusted results in Table 2). There were no significant associations among age, race, service era, employment status, violent behavior, or number of self-reported health problems and cannabis use. Veterans who were unmarried and those who smoked cigarettes or used at least one other drug were more likely to report use of cannabis in the past 6 months. Higher levels of combat exposure, lower quality of life, and more symptoms of hazardous alcohol use, depression, and suicidal ideation were also associated with increased likelihood of using cannabis. Higher clinician-rated global PTSD severity ratings and higher levels of PTSD Cluster C avoidance symptoms were associated with increased likelihood of using cannabis, but CAPS total score, reexperiencing, and hyperarousal symptoms were not associated with cannabis use.
Association of Demographic, Substance Use, and Clinical Characteristics With Cannabis Use Among Veterans Seeking Help for PTSD
Next, logistic regression models controlling for age, race, service era, and combat exposure were run (see adjusted results in Table 2). After accounting for these variables, marital status, smoking, other drug use, hazardous alcohol use, PTSD clinician-rated global severity, depressive symptoms, and suicidality remained associated with past 6-month cannabis use. Specifically, veterans who were unmarried, those who smoked, and those who used drugs other than cannabis were more likely to report use of cannabis in the past 6 months. Greater hazardous alcohol use, greater clinician-rated global PTSD severity, more severe depressive symptoms, and higher levels of suicidality were also all associated with increased likelihood of using cannabis.
Post hoc analyses were done to further examine the correlates of daily cannabis use compared to less frequent use among participants who reported using cannabis in the past 6 months (n = 105). In this series of logistic regression analyses, less-than-weekly cannabis use served as the reference category. In unadjusted models, younger veterans and those who served in Operation Enduring Freedom (OEF)/Operation Iraqi Freedom (OIF) (compared to Vietnam) were more likely to report daily use. Veterans who smoked cigarettes were less likely to report daily cannabis use. The association between cigarette smoking and daily cannabis use remained when controlling for age, race, service era, and combat exposure.
DiscussionThe purpose of this study was to examine the prevalence of cannabis use and its psychological and functional correlates among a large sample of veterans seeking treatment at a VA PTSD specialty clinic. Overall, 14.6% of participants reported using cannabis in the past 6 months. After controlling for age, race, service era, and combat exposure, past 6-month cannabis use was associated with marital status, smoking, other drug use, hazardous alcohol use, clinician-rated global PTSD severity, depressive symptoms, and suicidality. While multiple studies have documented associations between cannabis use disorders and trauma-related symptoms (Boden et al., 2013; Bonn-Miller et al., 2012), relatively few have examined the prevalence and correlates of cannabis use outside of samples with a diagnosed substance use disorder, particularly in veteran populations. Results from the present study showed that cannabis use among veterans seeking specialty treatment for PTSD was associated with severity of mood and trauma-related symptoms, as well as with use of other substances.
These results are consistent with a self-medication theory (Boden et al., 2013) in which cannabis use may serve an avoidance function for veterans struggling with symptoms of PTSD. In addition, the association between cannabis use and Cluster C avoidance is consistent with research showing an association between substance use history and avoidance coping, which may put individuals at greater risk of PTSD (Hruska, Fallon, Spoonster, Sledjeski, & Delahanty, 2011). These findings underscore the importance of identifying individuals whose cannabis use may be serving to self-medicate, and perhaps inadvertently to perpetuate symptoms of PTSD (Bonn-Miller, Boden, Vujanovic, & Drescher, 2013).
Previous research has consistently found associations between hyperarousal symptoms and cannabis use (Bonn-Miller et al., 2013; Bremner, Southwick, Darnell, & Charney, 1996), which was not replicated in the present study. This discrepancy may reflect differences in the sample used in the present study (i.e., individuals seeking PTSD specialty treatment), compared to previous studies that have primarily included individuals diagnosed with a cannabis use disorder. Furthermore, it is notable that cannabis use in the present study was associated with clinician-rated global PTSD severity, but not with CAPS total score. This may indicate that clinicians took into account other comorbid conditions (e.g., depression, substance use) in rating global PTSD severity, which may have artificially increased its association with cannabis use.
This study extends previous research documenting past-year prevalence and correlates of cannabis use among veterans referred from primary care for behavioral health assessment. Past 6-month prevalence rates in the current study (14.6%) were slightly higher than the past-year prevalence found in previous research (11.5%; Goldman et al., 2010), which may be attributable to differences in the sample composition, including the exclusion of female participants, as well as the possibility that individuals presenting to a PTSD specialty clinic may show more severe anxiety and mood symptoms than those being referred from primary care. Furthermore, a large percentage (27.6%) of participants who reported cannabis use in the present study reported daily use. However, the sample size for daily cannabis users was relatively small and differences between daily and other users should continue be examined in future studies.
Findings from the present study may have important implications for treatment of cannabis use and associated problems within the VA system, particularly in light of recent research showing that utilization of specialty substance use treatments has decreased even as use of cannabis has increased (Bonn-Miller et al., 2012). Given that many cannabis users may not present in substance use clinics, perhaps cannabis use disorder can be identified and treated in other clinics (e.g., PTSD clinics) where these individuals are likely to present for treatment of associated mental health issues. Results from this study may also have policy implications, in an era when marijuana has been suggested as a potential treatment for PTSD and other mental health conditions, and when at least one state has PTSD as an approved condition for the use of medical marijuana. Although results from the present study are correlational and cannot imply a causal relationship, the data presented here suggest that veterans who are using marijuana may be doing more poorly across several important life domains (e.g., marital status, mental health symptoms) compared with those who report no cannabis use.
Several limitations of the present study deserve mention. First, analyses were restricted to male participants because of the small number of female veterans in the sample. In addition, data were not available to determine how many individuals in the current sample met criteria for a substance use disorder. Furthermore, additional research on the validity of veteran self-reports is needed, as prior research in this area (e.g., Calhoun et al., 2000) may not apply given the current military and VA climate surrounding substance use. Although many veterans in the present study reported on their substance use, it remains possible that the observed prevalence is an underestimate. In addition, there is a risk of Type I error. Effect sizes (odds ratios) are included to indicate the magnitude of each effect and it is encouraging that results are largely consistent with those found in previous studies. Nevertheless, associations should be replicated in future study.
Despite these limitations, the present study extends previous work by examining the prevalence and correlates of cannabis use among individuals seeking specialty PTSD treatment. It provides some of the first data on cannabis use in this patient group. More work is needed to determine the causes and consequences of cannabis use in this population.
Footnotes 1 Additional analyses were conducted to test whether marital status, smoking, other drug use, hazardous alcohol use, depressive symptoms, and suicidality remained associated with marijuana use after adjusting for clinician-rated global PTSD severity. All findings remained significant.
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Submitted: May 5, 2015 Revised: November 1, 2015 Accepted: December 2, 2015
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
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Accession Number: 2016-24705-005
Digital Object Identifier: 10.1037/adb0000154
Record: 46- Prevalence and correlates of transactional sex among an urban emergency department sample: Exploring substance use and HIV risk. Patton, Rikki; Blow, Frederic C.; Bohnert, Amy S. B.; Bonar, Erin E.; Barry, Kristen L.; Walton, Maureen A.; Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014 pp. 625-630. Publisher: American Psychological Association; [Journal Article] Abstract: Men and women involved in transactional sex (TS) report increased rates of HIV risk behaviors and substance use problems as compared with the general population. When people engaged in TS seek health care, they may be more likely to utilize the emergency department (ED) rather than primary care services. Our goal was to examine the prevalence and correlates of TS involvement among an ED sample of men and women. Adults ages 18–60 were recruited from an urban ED, as part of a larger randomized control trial. Participants (n = 4,575; 3,045 women, 1,530 men) self-administered a screening survey that assessed past 3-month substance use (including alcohol, marijuana, illicit drugs, and prescription drugs) and HIV risk behaviors, including TS (i.e., being paid in exchange of a sexual behavior), inconsistent condom use, multiple partners, and anal sex. Of the sample, 13.3% (n = 610) reported TS within the past 3 months (64.4% were female). Bivariate analysis showed TS was significantly positively associated with alcohol use severity, marijuana use, and both illicit and prescription drug use, and multiple HIV risk behaviors. These variables (except marijuana) remained significantly positively associated with TS in a binary logistic regression analysis. The prevalence of recent TS involvement among both male and female ED patients is substantial. These individuals were more likely to report higher levels of alcohol/drug use and HIV risk behaviors. The ED may be a prime location to engage both men and women who are involved in TS in behavioral interventions for substance use and sexual risk reduction. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Prevalence and Correlates of Transactional Sex Among an Urban Emergency Department Sample: Exploring Substance Use and HIV Risk / BRIEF REPORT
By: Rikki Patton
The Substance Abuse Research Center, Department of Psychiatry, and School of Social Work, University of Michigan;
Frederic C. Blow
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Amy S. B. Bohnert
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Erin E. Bonar
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Kristen L. Barry
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Maureen A. Walton
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Acknowledgement: Rikki Patton is now at the University of Akron.
This investigation was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award T32 DA007267 and NIDA #026029. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Transactional sex (TS) involvement, defined as the exchange of a sexual behavior for money, drugs, or other needs, is associated with increased likelihood of substance use issues (Clarke, Clarke, Roe-Sepowitz, & Fey, 2012; Wechsberg et al., 2009), and greater risk of contracting sexually transmitted infections (STIs), including HIV (Bobashev, Zule, Osilla, Kline, & Wechsberg, 2009; Shannon et al., 2008). The relationship between TS involvement and substance abuse is complex, with research suggesting that substance abuse may act as the antecedent behavior to TS involvement for some individuals, but as a consequence to being involved in TS for others (Belcher & Herr, 2005; Potterat et al., 1998). Further, substantial proportions of substance users report TS involvement, with 25% of male crack users and over 40% female crack-cocaine users in various samples reporting TS within the past 30 days (Edwards, Halpern, & Wechsberg, 2006; Leukefeld, 1999; Logan & Leukefeld, 2000). Regardless of the motivation for use, these prevalence rates suggest individuals who engage in TS are a population vulnerable to substance abuse problems and engaging TS-involved individuals into substance abuse prevention and intervention programs may provide a crucial avenue for addressing both their substance use and their risky sexual behaviors.
TS-involved individuals are considered a difficult-to-reach population (Benoit, Jansson, Millar, & Phillips, 2005). Prior research suggests that substance-abusing women involved in TS tend to seek health care services through the emergency department (ED) compared with other substance-abusing women (Burnette, Lucas, Ilgen, Frayne, Mayo, Weitlauf, 2008). For instance, 39%–56% of women who self-identified as sex workers reported visiting the ED recently or as a result of their TS involvement (Raymond, Hughes, & Gomez, 2001; Shannon, Bright, Duddy, & Tyndall, 2005). These findings suggest that the ED may be a prime location for engaging individuals involved in TS. Less is known about the degree to which men engaged in TS utilize health care services, although prior reports have suggested that, among a sample of men receiving substance abuse treatment, TS-involved men who were involved in TS reported greater likelihood of use of inpatient mental health services compared with other patients, but not emergency services (Burnette et al., 2008).
One study examining the utility of a brief intervention with drug-positive men and women recruited through the ED who reported either cocaine or heroin use stated that approximately 12% of patients reported TS involvement in the past 30 days (Bernstein et al., 2012). Although this study highlights the prevalence of TS involvement within an ED setting among a high-risk substance-using group, findings are limited in their generalizability and applicability to the entire ED patient population.
Present StudyThe current study aims to address the gaps in the literature by, first, evaluating the prevalence of TS involvement among broad a sample of both men and women seeking care in an ED. In addition, given the prior research regarding increased HIV risk and substance abuse associated with TS involvement (Bobashev et al., 2009; Shannon et al., 2008), we evaluated whether substance abuse and HIV risk indicators differed among individuals involved in TS compared with other ED patients. We hypothesized that those ED patients who reported TS involvement would also report higher rates of substance use and HIV-related risk behaviors than those who did not report TS.
Method Study Design and Setting
The current study used data collected as part of a screening survey aimed at identifying patients eligible for a randomized control trial of adult patients (ages 18–60) presenting to an ED located in an Academic Level 1 Trauma Center a Midwestern city with similar rates of crime and poverty as other large cities. The study was approved and conducted in compliance with Institutional Review Board (IRB) requirements. A Certificate of Confidentiality was obtained for this study. Data were collected from February, 2011 to March, 2013. Research staff approached participants and described the study; those who were interested in participating provided written informed consent and self-administered a 15-min computerized screening survey. Participants were compensated for screening with a gift valuing $1 (e.g., playing cards or hand lotion). Patients who presented with acute psychosis, acute sexual assault, medically unstable, or who were in police custody were excluded from screening.
Measures
Transactional sex involvement
TS involvement was assessed using the participant’s responses to the following question from the HIV Risk-taking Behavior Scale (HRBS; Ward, Darke, & Hall, 1990)—“In the past three months, how often have you used condoms when you have been paid for sex?” Respondents who answered “no paid sex” were recoded into the group labeled non-TS group and all other respondents were recoded into the group labeled as TS group.
Demographic variables
Gender, race, age, household income, marital status, current employment, sexual orientation, and education level were queried using items from validated surveys (e.g., National Survey of Drug Use and Health, Office of Applied Studies, 2009; Psychiatric Outcomes Module: Substance Abuse Outcomes Module, Smith et al., 1996; Global Appraisal of Individual Needs, version 5.4.0., 2006).
Risk Variables
Reason for ED visit
Participants were asked a yes/no question regarding whether or not their visit to the ED was injury related, referring to cuts, bruises, broken bones, and so forth.
Alcohol and drug use
Alcohol use severity over the past 3 months was assessed with the 10-item Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, De La Fluente, & Grant, 1993). Questions assessed frequency of alcohol use, number of drinks consumed on a typical drinking day, frequency of having five or more drinks, and negative consequences due to drinking. Participants’ responses on these items were summed to create a single total score reflecting alcohol use severity (Saunders et al., 1993). Cronbach’s alpha for this measure in the current sample was .90.
The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST; WHO ASSIST Working Group, 2002) was used to measure frequency of drug use. Respondents were asked if they had used the following drugs at least once in the past 3 months—cocaine, marijuana, methamphetamines, hallucinogens, inhalants, prescription stimulants, prescription sedatives, prescription opioids, street opioids, or any other drugs. Due to the distribution of the data, with low frequencies of use for all illicit drugs, excluding marijuana, and nonmedical use of prescription drugs, these variables were refined into the following groups representing using at least once in the past 3 months: (a) marijuana, (b) other illicit drugs, and (c) nonmedical use of prescription drugs.
HIV risk behaviors
Sexual and drug use HIV risk behaviors during the past 3 months were assessed using items selected from the HIV Risk-Taking Behavior Scale (HRBS; Ward, Darke, & Hall, 1990). Specifically, injection drug use, number of sexual partners, inconsistent condom use, and anal sex were queried. Each variable was dichotomized to denote engaging in the risky behavior within the past 3 months. STIs were measured by asking respondents if a doctor or other medical professional ever told the patient that they had an STI, such as chlamydia, gonorrhea, herpes, or syphilis. Respondents answered yes or no to this question (see Substance Abuse & Mental Health Services Administration, 2009).
Data Analysis Plan
Data were analyzed using SAS version 9.3 (SAS Institute Inc., 2012). First, demographic characteristics of the sample were examined. Variable distributions were examined for normality and appropriate statistics were used. Bivariate analyses, including chi-square and independent samples t tests, were conducted to determine if there were significant differences between the TS group and other participants on demographic characteristics, substance use, and HIV risk behaviors. Finally, a hierarchical logistic regression was conducted to determine the associations of TS involvement with demographic and risk variables. A hierarchical model was used in order to control for demographic characteristics in later steps. Demographic factors were entered on Step 1, followed by substance use variables on Step 2, with sex risk behaviors entered on Step 3. All variables that were significant in bivariate analyses were retained in the regression model. Model fit statistics indicated no evidence of multicollinearity.
Results Sample Characteristics
As part of the larger randomized controlled trial (RCT), 6,161 individuals were approached for screening, of whom, 4,575 (74.3%) agreed to complete the screening survey. Respondents completing the screening survey were compared with those who were missed and who refused on gender and race. Males were more likely to be missed (χ2 = 94.1; p < .0001) and to refuse participation (χ2 = 30.95; p < .0001). There were no significant differences by race based on those missed (χ2 = 2.576; p = .11) or refused (χ2 = 3.526; p = .06). Per privacy-related policies of the local IRB, we were not able to collect any additional data about characteristics of patients who refused without written informed consent. See Table 1 for sample characteristics.
Frequencies and Bivariate Analysis for the Whole Sample and Subsamples
Bivariate Analysis
See Table 1 for a full summary of findings from the bivariate analyses. Chi-square/t test analyses indicated there were several significant differences between the TS group and the non-TS group. Participants in the TS group were more likely to report an educational level of high school completion or below (χ2 = 22.9; df = 1), an annual income more than $20,000, and to be married (χ2 = 10.8; df = 1). The TS group had higher scores on all substance use and HIV risk measures as compared with the non-TS group. There were no significant differences regarding gender, race, age, or sexual orientation between the two groups.
Regression Analysis
The hierarchical logistic regression analysis was conducted in order to examine the relationship between transactional sex involvement and Step 1: demographic characteristics (gender, race, educational level, income, marital status, and ED presentation); Step 2: substance use variables (alcohol use severity, any marijuana use, any other illicit drug use, and any prescription drug use); and Step 3: sexual risk behaviors (inject drugs, diagnosed with an STI, number of sexual partners, inconsistent condom use, and anal sex). Although gender and race were not significant in the bivariate analysis, these variables were included in the multivariate analysis due to the strong evidence in prior literature regarding the higher probability of women and racial minorities engaging in transactional sex behaviors. Model fit statistics indicated that each step improved the model (see Table 2). In Step 1, being Caucasian and income (making less than $20,000) were negatively associated with TS involvement (AOR = 0.80 and 0.83, respectively). Having a high school education or less (AOR = 1.59) and being married or living together as married (AOR = 1.43) were positively associated with TS involvement. No other demographic variables were significant.
Hierarchical Binary Logistic Regression Assessing Correlates of Transactional Sex Involvement
With the addition of substance abuse variables in Step 2 the following variables were positively associated with TS involvement: alcohol use severity (AOR = 1.03), illicit drug use (AOR = 2.62), prescription drug use (AOR = 1.53), educational status (AOR = 1.58), and marital status (AOR = 1.53). Race (being Caucasian) and income were negatively associated with TS involvement (AOR = 0.78 and 0.79, respectively).
In the final model including HIV-risk variables, the following variables were positively associated with TS involvement: alcohol use severity (AOR = 1.03), illicit drug use (AOR = 2.19), prescription drug use (AOR = 1.60), ever injecting drugs (AOR = 1.70), inconsistent condom use (AOR = 9.75), and engaging in anal sex (AOR = 1.77). Individuals involved in TS were less likely to be Caucasian (AOR = 0.75), earn less than $20,000 annually (AOR = 0.68), and to ever be diagnosed with an STI (AOR = 0.58), and were more likely to report an education level of high school diploma or less (AOR = 1.65). Nonsignificant variables in the final model included gender, marital status, reason for presenting to the ED, marijuana use, and having multiple sexual partners in the past 3 months.
DiscussionThis study presents novel findings regarding the prevalence and correlates of TS involvement within an urban ED sample of adult men and women. Findings indicated that 13.3% of patients sampled had engaged in TS within the past 3 months and, of those, gender distribution was similar to the larger sample. Findings also showed that patients involved in TS were more likely to report substance use and HIV-risk behaviors compared with other patients.
That more than one in 10 patients presenting to this urban ED reported recent TS involvement has several implications for engagement and treatment. Most research examining TS involvement includes substance-abusing samples only, with rates of TS involvement ranging from 12%–44% (e.g., Bernstein et al., 2012; Burnette et al., 2008). The current sample included individuals with and without substance abuse problems and the prevalence was still within the range of substance-abusing only samples. Thus, the ED may be a useful venue for engaging individuals involved in transactional sex in brief interventions without limiting services to only those individuals who also present with substance abuse problems. Additionally, the current findings showed that TS involvement was not associated with gender, suggesting that both men and women involved in TS may be reached in one location—in the ED.
The present findings for ED patients also support previous reports of increased alcohol and illicit drug use among individuals involved in TS in the general population (Clarke et al., 2012; Wechsberg et al., 2009). A unique finding from this study is that TS involvement was related to increased odds of misusing prescription drugs, including opioids and sedative/hypnotics. Given these findings and the recent increases of prescription drug misuse throughout the United States (Gu, Dillon, & Burt, 2010; SAMHSA, 2009), additional research is needed to understand this association in order to appropriately tailor intervention strategies to address this emerging pattern of substance use. Prior research suggests that individuals who engage in TS may have higher levels of substance use for multiple reasons, including coping with the stresses related to TS involvement (Belcher & Herr, 2005; Burnette et al., 2008; Wechsberg et al., 2009). Alternatively, TS may be partially a result of greater involvement in substance use among impoverished populations as a means to maintain substance use (Clarke et al., 2012; Potterat et al., 1998). Although the causal nature of this relationship requires future study, our findings suggest that the ED may be an appropriate venue for interventions addressing substance use and TS, both of which are associated with risk for STIs and HIV infection, among this vulnerable population.
Finally, the present findings also showed a significant association between TS and HIV-risk behaviors, including injecting drugs, STI diagnosis, inconsistent condom use, and anal sex among patients recruited from the ED, which are consistent with prior work with substance-using samples (Bobashev et al., 2009; Shannon et al., 2008). Interestingly, current findings indicate that men and women who engage in TS are less likely to have received a formal diagnosis of a STI, despite also being more likely to report inconsistent condom use. This may not necessarily reflect a true decreased risk of STI in this population, however, because it is possible that individuals from this vulnerable population may have more barriers to STI screening and treatment services (Kurtz, Surratt, Kiley, Marion, & Inciardi, 2005). Overall, current findings suggest that the ED may be a relevant site for intervention efforts focused on sexual risk reduction among men and women, particularly those who engage in TS given the high prevalence (91.8%) of inconsistent condom use, one of the most potent predictors of HIV/STI.
Limitations and Future Directions
Although the present study augments current literature by examining the prevalence and correlates of TS involvement among men and women in the ED, several limitations should be noted. The criterion we used to define TS was limited by the use of a single item assessment regarding condom use with partners who paid for sex. Although this question asked about recent involvement in exchanging sex for money, the depth of involvement in sex work for these participants is unknown. In addition, this question only inquired about being paid for sex, and may not have identified those who trade sex for drugs or other goods. Further, one of the exclusion criteria for the original study was presenting to the ED for acute sexual assault. Individuals involved in exchanging sex for money experience repeated sexual assaults (Dalla, Xia, & Kennedy, 2003; Karandikar & Prospero, 2010) and these exclusion criteria may have biased the sample, producing a conservative estimate of the prevalence of TS involvement among ED patients. Despite these limitations, the results showed that 13.3% of individuals attending the ED, including both men and women, report engaging in TS. Additionally, although our sample was racially diverse, these results may not be generalizable to other populations or racial/ethnic groups (e.g., Hispanics, Asians), or to other geographic locales due to recruitment from a single site within an ED located in an economically depressed urban area. Further, the analysis was cross-sectional in design, thereby limiting our ability to understand causal relationships among TS and substance use or other factors.
To address these limitations, future research should explore the relationship between TS involvement and use of the ED longitudinally in order to determine how these individuals use the ED to meet their needs and their connection to other services, such as substance abuse treatment. More in-depth assessment of TS involvement is warranted to allow further understanding of how interventions can be developed to effectively intervene with individuals in the ED who engage in high-risk sexual behaviors and substance use. Additionally, given that so many men receiving services in the ED also reported TS involvement, it may be helpful to explore gender differences in TS to inform prevention, behavioral intervention, and medical screening and treatment of STIs. Focusing on these individuals, defined as a difficult-to-reach population, for intervention services in the ED may prove to be a fruitful area for research and practice. Future research is needed to develop and implement combined HIV risk and substance use intervention programs tailored for patients in the ED engaged in both behaviors.
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Submitted: April 23, 2013 Revised: August 6, 2013 Accepted: September 16, 2013
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 625-630)
Accession Number: 2014-24742-020
Digital Object Identifier: 10.1037/a0035417
Record: 47- Problem gambling and violence among community-recruited female substance abusers. Cunningham-Williams, Renee M.; Ben Abdallah, Arbi; Callahan, Catina; Cottler, Linda; Psychology of Addictive Behaviors, Vol 21(2), Jun, 2007 pp. 239-243. Publisher: American Psychological Association; [Journal Article] Abstract: Problem gambling (PG) may be associated with depression, victimization, and violence characterizing a substance-abusing lifestyle. The study explored associations of PG with these correlates among heavy-drinking and drug-using out-of-treatment women recently enrolled in 2 National Institutes of Health-funded, community-based HIV prevention trials. Female substance abusers with PG (n = 180) and without PG (NPG; n = 425) were examined according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994). Whereas PGs had higher rates of each correlate than did NPGs, significant associations existed for antisocial personality disorder, specifically for violent tendencies. Logistic regression indicated that substance abusers with violent tendencies were about 3 times as likely as those without such tendencies to be PGs, after controlling for sociodemographics. Future research addressing whether underlying constructs, confounding variables, or interactions exist will further specify PG risk and inform prevention and intervention efforts. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Problem Gambling and Violence Among Community-Recruited Female Substance Abusers
By: Renee M. Cunningham-Williams
George Warren Brown School of Social Work, Washington University;
Arbi Ben Abdallah
Department of Psychiatry, Washington University School of Medicine
Catina Callahan
Department of Psychiatry, Washington University School of Medicine
Linda Cottler
Department of Psychiatry, Washington University School of Medicine
Acknowledgement: An earlier version of this article was presented at the annual meeting of the College on Problems of Drug Dependence, San Juan, Puerto Rico, June 16, 2004. This work was supported in part by National Institute on Drug Abuse Grant K01 DA 00430 to Renee M. Cunningham-Williams and Grant R01 DA 11622 to Linda Cottler and by National Institute on Alcohol Abuse and Alcoholism Grant R01 AA 12111 to Linda Cottler. Technical assistance was provided by the Center for Mental Health Services Research at the George Warren Brown School of Social Work, Washington University, with funding from National Institute of Mental Health Grant 5P30 MH068579 and by Karen L. Dodson, Managing Editor and Director of Academic Publishing Services, Washington University School of Medicine. We also acknowledge the contributions of the project staff and the participants of the Women Teaching Women and Sister-to-Sister projects.
High rates of co-occurring disorders are clearly evident among users of illegal substances (Compton, Thomas, Conway, & Culliver, 2005). Several investigations have found high rates of depression and suicidal ideation (Cottler, Campbell, Krishna, Cunningham-Williams, & Ben-Abdallah, 2005), antisocial and criminal activity (Fishbein, 2000), violent perpetration and victimization (Chermack, Walton, Fuller, & Blow, 2001), and trauma and posttraumatic stress disorder (Fullilove et al., 1993). In fact, there appears to be an intersection of several of these behaviors and disorders for African American female substance abusers (Johnson, Cunningham-Williams, & Cottler, 2003).
According to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994), substance abusers are also at increased risk for gambling problems (National Research Council, 1999), with rates of gambling problems and pathological gambling disorder (PGD) being 22% and 11%, respectively (Cunningham-Williams, Cottler, Compton, Spitznagel, & Ben Abdallah, 2000). Additional vulnerabilities for gambling problems include racial/ethnic minority status, poverty, and male gender (Welte, Barnes, Wieczorek, Tidwell, & Parker, 2002). There is a dearth of research on problem gambling (PG) risk specifically for women, particularly those of color who abuse substances, despite their differing gambling profiles (Tavares et al., 2003), although the National Epidemiologic Survey on Alcohol and Related Conditions found associations of DSM–IV PGD with substance use disorder and internalizing disorders, such as major depressive episode and generalized anxiety disorder, that were stronger for women than for men (Petry, Stinson, & Grant, 2005).
This article explores and describes for the first time the association of PG with depression, victimization, and violence in a sample of out-of-treatment, female substance abusers who are predominately poor, young, and African American. We specifically hypothesize that female substance abusers with PG will have significantly higher rates of depression, victimization, and violence exposure compared to those without PG (NPG).
Method Sample and Study Description
Community-based outreach methods (Cunningham-Williams et al., 1999) were used to recruit women 18 years of age and older for enrollment in one of two National Institutes of Health–funded, community-based, HIV prevention trials developed and conducted from 1998–2004 in St. Louis, Missouri: (a) Women Teaching Women (n = 550), which targeted cocaine-, opiate-, and amphetamine-using women determined by a positive urinalysis or fresh injection track marks; and (b) Sister-to-Sister (n = 376), which targeted urine drug-negative women who were heavy alcohol users with scores ≥4 on the Alcohol Use Disorders Identification Test (Babor, de la Fuente, Saunders, & Grant, 1992).
Trained nonclinicians administered the computerized version of the Washington University–Risk Behavior Assessment at Week 1 (Baseline 1) and the computerized Diagnostic Interview Schedule (C-DIS, Version 4; Robins et al., 1999) at Week 2 (Baseline 2). Interviewers also collected additional information at Week 2 on psychiatric disorders (including gambling problems, depression, antisocial personality disorder [ASPD], victimization, and other aspects of violence, life events, and cocaine-related locus of control and expectancies). After the assessments, at Week 2, women received sexually transmitted disease test results and standard posttest counseling, with referrals offered for HIV-positive women (n = 22) who were not randomized for the study intervention. Participants were reinterviewed at 4 and 12 months postintervention and were remunerated for their time. All procedures were approved by the Washington University Institutional Review Board.
For this article, we used three composite measures as independent variables, namely lifetime major depression, violence, and victimization. Using the C-DIS for PGs (i.e., with 1–10 DSM–IV PGD criteria) and for NPGs (i.e., 0 criteria), we assessed lifetime reports of DSM–IV major depression that includes five items assessing suicidality. For this article, we were also interested in two additional composite measures, namely violence and victimization. Therefore we not only assessed DSM–IV ASPD but also characterized violence in terms of nonviolent antisocial acts and violent tendencies, using selected items from both these criteria and from the self-developed Violence Exposure Questionnaire, a compilation of violence-related items from several investigations by others in the field. Violence was also characterized by the final composite variable of childhood victimization before age 15 as operationalized by additional Violence Exposure Questionnaire items.
Statistical Analyses
We used SAS Release 8.2 for univariate and chi-square analysis of the association of PG with composite measures of depression, violence, and victimization. We also tested the hypothesis of increased risk for PG compared to NPG, using four ordered logistic models based on the proportional odds assumption. In Model I, we controlled for the effects of sociodemographics and included only those composite measures found to be significantly related to PG after post hoc Bonferroni correction. We then further specified Model I in three subsequent models (Models II–IV) by examining the individual items comprising the significant composite measures included in Model I. We then compared with Model I the model fit statistics of these latter models in order to determine the best-fitting and most parsimonious model of PG risk in this sample.
Because women enrolled in both studies were recruited from the same target areas and did not significantly differ in sociodemographics or gambling behaviors, we combined them into one sample for further analysis. The final combined study sample, n = 849 (Sister-to-Sister, n = 348; Women Teaching Women, n = 501), excludes 55 women with incomplete baseline interviews. For this article, we further excluded women with incomplete gambling data (n = 12). Additionally, we excluded those who reported gambling or betting five or fewer times during their lifetimes (n = 232) because this was the screening threshold that admitted respondents to this section of the C-DIS. We also wanted to be consistent with the addiction section of the C-DIS. Further, this threshold was chosen at the time of the Epidemiological Catchment Area study (Robins & Regier, 1991) to represent a group that did not have clinically meaningful symptoms. The final data analysis sample is 605.
ResultsSociodemographically, PGs did not differ significantly from NPGs in that these female substance abusers were young, never-married mothers who were jobless in the previous 12 months, poor, and with low education (mean age: NPG = 36.3 years, SD = 9.0; PG = 36.0 years, SD = 8.1; never married: NPG = 35.6%, PG = 35.6%; ≥1child: NPG = 78.4%, PG = 84.4%; jobless in past 12 months: NPG = 49.7%, PG = 56.1%; annual household income ≥$3,999: NPG = 43.2%, PG = 45.0%; >high school education: NPG = 50.6%, PGs = 57.2%). Yet, this predominately African American sample was statistically more represented among the PGs than the NPGs (87.8% vs. 76.2%; χ2 = 10.40, p = .0013).
The majority of the sample did not experience any gambling problems (0 criteria: 70.25%, n = 425). However, 21.16% of the sample met between 1 and 4 DSM–IV PGD criteria (1 criterion: 7.93%, n = 48; 2 criteria: 5.79%, n = 35; 3 criteria: 4.63%, n = 28; 4 criteria: 2.81%, n = 17), and the remaining 8.59% of the sample met the DSM–IV PGD threshold of 5 or more criteria (8.59%, n = 52). The first gambling problem was experienced as early as age 10, and the average age of onset was 28.6 years (SD = 8.86; range = 10–50). Although less than 10% of PGs met criteria for DSM–IV PGD (n = 52), PGs averaged a clinically significant number of criteria, falling just below the diagnostic cutoff of 5 criteria for PGD (M = 3.47; SD = 2.45; range = 1–10).
Associations of Gambling Problems With Depression, Victimization, and Violence
Although PGs had a higher lifetime major depression rate than NPGs, this difference was not statistically significant (Table 1). Similarly, although nearly half of the sample experienced being a victim of violence, there was no significant variation by PG status. However, we also considered a final composite variable of violence that we broadly conceptualized as nonviolent and violent antisocial behaviors operationalized by DSM–IV ASPD criteria. ASPD was significantly associated with PG (at the Bonferroni-adjusted significance level of p ≤ .02). In examining the items individually, our findings showed that PGs were no more likely than NPGs to have higher rates of the nonviolent antisocial acts, yet the opposite was true for items indicating violent tendencies. For example, even after post hoc correction, we found that although PGs were no more likely to own a gun, they were more likely than NPGs to have access to a gun and to carry one. Furthermore, compared to NPGs, PGs in this sample were more likely to be irritable or aggressive, to lack remorse, to threaten to hit or throw something at someone, and to have used drugs before their last fight.
Lifetime Depression, Victimization, and Violence Among Female Substance Abusers With and Without Gambling Problems (n = 605)
Logistic Model of Risk for Gambling Problems
Table 2 shows four separate logistic regression models predicting risk for PG, using NPG as a reference category. In the first multivariate model examined (Model I), we controlled for sociodemographics (i.e., age, race/ethnicity, education) and more closely examined the unique contribution of ASPD, as it was the only significant composite variable, even after Bonferroni correction, among the three composite variables bivariately examined. Logistic regression results indicated that after controlling for sociodemographics, there was an increased risk for PG among those with ASPD (odds ratio = 3.25; Model I). We further specified these results by examining the violent tendency subscale both with (Model II) and without (Model III) the subscale of nonviolent antisocial acts. Although Model III had a slightly better fit for these data than did Model II, neither model was superior to Model I with its larger likelihood ratio χ2(30.36, p < .0001); larger Nagelkerke’s R2 (0.07); and smaller Akaike information criterion (712.385).
Logistic Regression Predicting PG Status of Female Substance Abusers (n = 605)
Finally, we used Model IV to investigate whether the inclusion of only the individual violent tendency items (rather than the subscale) would provide a superior model of PG risk in this sample. Our findings show that although Model IV was the strongest model examined, in that it had better model fit statistics (likelihood ratio χ2 = 56.76, p < .0001; Nagelkerke’s R2 = 0.1369; AIC = 650.328), with 14 degrees of freedom, it was not the most parsimonious model. Furthermore, among the 11 violent tendency items in Model IV, only drug use before engaging in a fight remained as a significant predictor of increased PG risk. Thus, Model I, which showed that those with ASPD were about three times as likely as those without to be a PG, was the best model of PG risk for this sample of female substance abusers.
DiscussionThis is the first report testing the hypothesized association of PG with depression, victimization, and violence among a sample of primarily African American, low-income, out-of-treatment, female substance abusers. We found that violence in many forms, particularly in the form of antisocial behaviors and ASPD, permeates the lives of female substance abusers, which concurs with findings from other research (Fullilove et al., 1993). Although we did not find a significant association of victimization with PG, we did find that after controlling for sociodemographics, a model of violence in the form of ASPD was significantly associated with the increased likelihood of PG in this sample, thus supporting our hypothesis. Furthermore, although this model was the best model tested for understanding PG risk in this sample, it was primarily driven by items relating to violent tendencies, particularly the intersection of drug use and fighting behavior. These results imply that screening and intervention efforts need to be specifically targeted to out-of-treatment female substance abusers at highest risk for PG, particularly those who are African American and have violent tendencies. Assessing ASPD generally and violent tendencies specifically may be prudent not only for preventing potential aggressive and violent behavior among female substance abusers but also for impacting PG behavior among them. Future research that teases out underlying factors and/or significant interactions may further specify PG risk among female substance abusers.
The data are consistent with other investigations of gambling behavior within a substance abusing population showing a high prevalence of both PG and PGD (McCormick, 1993). Our finding of high rates of depression among substance abusers and among substance abusers who gamble is supported by the work of others using different measurement tools and samples drawn from treatment settings (Maccullum & Blaszczynski, 2003) and from the general population (Petry et al., 2005). Yet, the hypothesized relationship of significant associations of PG with depression was unsupported in this sample, potentially due to methodological differences in measurement and in sampling strategies used in this study and in the work of others.
These results are presented in the context of several important limitations. First, although this is a unique and understudied sample, the results cannot be generalized to other substance abusers (e.g., men and substance abusers in treatment) or to the general population of gamblers. Also, although the average number of criteria met in this sample fell within the subsyndromal level (i.e., <5 criteria), the small sample of those meeting criteria for DSM–IV PGD precluded a plan that separately analyzed risk specifically for PGD. Although effective for showing increased risk at the binary level (i.e., no gambling problems vs. gambling problems), combining the two groups (those with ≥5 criteria and those with 1–4 criteria) into a single PG group does not allow for additional specificity regarding increased risk according to PG severity. Future research with a larger sample of more severe problem gamblers is warranted for further delineation of risk for PGD.
Additionally, these findings result from a secondary analysis using data combined from two studies that were neither designed as gambling studies nor exclusively comorbidity studies among substance abusers. We also did not have data on more specific explanatory variables (e.g., gambling activity type, frequency, personality traits, and risk-taking), and some included variables lacked psychometric data, thus potentially affecting the results. Including some otherwise excluded variables could have allowed for an exploration of the propensity for behavioral disinhibition, to which depression, victimization, violence, and PG may each contribute (Martins, Tavares, da Silva, Galetti, & Gentil, 2004).
Despite these limitations, our findings underscore the need for increased attention to the role of PG in the lives of minority female substance abusers, given that nearly 10% of them have met enough criteria to be diagnosed with lifetime DSM–IV PGD. Furthermore, our results show that not only are the lives of those in the sample characterized by violence in general but also that ASPD (specifically tendencies toward violence in the form of using drugs before fighting) is especially predictive of PG even after controlling for sociodemographic factors. Future research may be able to explore whether PG is the result of “escape gambling” for female substance abusers who are attempting to deal with experiences of violence in their lives or just one of several risky/antisocial behaviors accompanying untreated substance abuse among minority women.
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Cottler, L. B., Campbell, W., Krishna, V. A. S., Cunningham-Williams, R. M., & Ben-Abdallah, A. (2005). Predictors of high rates of suicidal ideation among drug users. Journal of Nervous and Mental Disease, 193, 431–437.
Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M., Desmond, D. P., Wechsberg, W., Zule, W. A., & Deichler, P. (1999). Reaching and enrolling drug users for HIV prevention: A multi-site analysis. Drug and Alcohol Dependence, 54, 1–10.
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Submitted: November 14, 2005 Revised: September 1, 2006 Accepted: September 3, 2006
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Source: Psychology of Addictive Behaviors. Vol. 21. (2), Jun, 2007 pp. 239-243)
Accession Number: 2007-08148-013
Digital Object Identifier: 10.1037/0893-164X.21.2.239
Record: 48- Project INTEGRATE: An integrative study of brief alcohol interventions for college students. Mun, Eun-Young; de la Torre, Jimmy; Atkins, David C.; White, Helene R.; Ray, Anne E.; Kim, Su-Young; Jiao, Yang; Clarke, Nickeisha; Huo, Yan; Larimer, Mary E.; Huh, David; The Project INTEGRATE Team; Psychology of Addictive Behaviors, Vol 29(1), Mar, 2015 pp. 34-48. Publisher: American Psychological Association; [Journal Article] Abstract: This article provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed 2 or more times from baseline up to 12 months, with varying assessment schedules across studies. This article describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo (MCMC) algorithms for 2-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single intervention studies. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Project INTEGRATE: An Integrative Study of Brief Alcohol Interventions for College Students
By: Eun-Young Mun
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Jimmy de la Torre
Department of Educational Psychology, Rutgers, The State University of New Jersey
David C. Atkins
Department of Psychiatry and Behavioral Sciences, The University of Washington
Helene R. White
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Anne E. Ray
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Su-Young Kim
Department of Psychology, Ewha Womans University
Yang Jiao
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Nickeisha Clarke
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Yan Huo
Department of Educational Psychology, Rutgers, The State University of New Jersey
Mary E. Larimer
Department of Psychiatry and Behavioral Sciences, The University of Washington
David Huh
Department of Psychiatry and Behavioral Sciences, The University of Washington
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Department of Educational Psychology, Rutgers, The State University of New Jersey;
Department of Psychiatry and Behavioral Sciences, The University of Washington;
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Department of Psychology, Ewha Womans University;
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Department of Educational Psychology, Rutgers, The State University of New Jersey;
Department of Psychiatry and Behavioral Sciences, The University of Washington
Acknowledgement: The project described was supported by Award Number R01 AA019511 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health. We thank Lisa A. Garberson, Caressa Slocum, and Yue Feng for their helpful comments on earlier drafts of this paper and for their help with data management. The Project INTEGRATE Team consists of the following contributors in alphabetical order: John S. Baer, Department of Psychology, The University of Washington, and Veterans’ Affairs Puget Sound Health Care System, Seattle, WA; Nancy P. Barnett, Center for Alcohol and Addiction Studies, Brown University; M. Dolores Cimini, University Counseling Center, The University at Albany, State University of New York; William R. Corbin, Department of Psychology, Arizona State University; Kim Fromme, Department of Psychology, The University of Texas, Austin; Joseph W. LaBrie, Department of Psychology, Loyola Marymount University; Matthew P. Martens, Department of Educational, School, and Counseling Psychology, The University of Missouri; James G. Murphy, Department of Psychology, The University of Memphis; Scott T. Walters, Department of Behavioral and Community Health, The University of North Texas Health Science Center; and Mark D. Wood, Department of Psychology, The University of Rhode Island.
This article provides an overview of a collaborative study entitled Project INTEGRATE. Project INTEGRATE is the first behavioral treatment research project to embrace recent advances in psychometrics and statistical methods (e.g., meta-analysis using individual participant-level data [IPD] or integrative data analysis [IDA]). The overall goals are to provide answers to evasive research questions (e.g., identification of mediational paths and subgroup differences), as well as to provide a built-in replication for the reported efficacy of brief motivational interventions (BMIs) for college student populations. The term IDA was coined by Curran and Hussong (2009) to highlight some of the unique promises, as well as challenges, that arise when combining studies in the psychological sciences. The term “meta-analysis using IPD” has been utilized more frequently in evaluating randomized control trials (RCTs) in medical research. We interchangeably use IDA and meta-analysis using IPD (or IPD meta-analysis) in the present article. This article does not report clinical treatment outcomes. Rather, we provide an overview of this research project and discuss the challenges encountered, steps taken to overcome these challenges, and lessons learned thus far. This overview sets the stage for articles that focus on clinical outcomes and mechanisms of behavior change to follow.
Available reviews of BMIs for college students have documented that BMIs (e.g., the Brief Alcohol Screening and Intervention for College Students; Dimeff, Baer, Kivlahan, & Marlatt, 1999) are effective in reducing alcohol use and related problems at least on a short-term basis (Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Cronce & Larimer, 2011). Furthermore, those delivered in person provide more enduring effects compared with computer-delivered feedback interventions, including computer-delivered normative feedback interventions and computer-delivered educational alcohol interventions (Carey, Scott-Sheldon, Elliott, Garey, & Carey, 2012). However, the estimated effect sizes of these brief interventions are fairly small (e.g., Cohen’s d ranging from 0.04 to 0.21 from random-effects models for outcome variables at short-term follow-up [4 to 13 weeks postintervention] of individually delivered interventions; Carey et al., 2007), and vary from study to study across key outcome variables such as alcohol use and alcohol-related problems. Furthermore, only a small subset of studies had a statistically significant effect when reanalyzed in a meta-analysis (Carey et al., 2007). Thus, there appears to be incongruence in the strength of the overall effect between single studies and meta-analysis studies.
Emerging evidence suggests that single studies may be more susceptible to biased statistical inference than previously thought. For example, recent meta-analytic studies examining the efficacy of antidepressant medication aptly demonstrate the potential pitfalls of relying on evidence only from single studies. Turner, Matthews, Linardatos, Tell, and Rosenthal (2008) meta-analyzed aggregated data (AD; e.g., effect size estimates) on antidepressant medication submitted to the Food and Drug Administration (FDA) and in published articles from 74 trials (12 drugs and 12,564 patients) that were registered with the FDA between 1987 and 2004. Their analyses indicated that effect sizes had been substantially overestimated in published articles. For example, whereas 94% of the 37 published studies reported a significant positive result, only 51% had a positive outcome according to the meta-analysis of the FDA data. On average, Turner et al. found a 32% difference in effect sizes between the FDA data and the published data. Moreno et al. (2009) further showed that this false-positive outcome bias was associated with publications, and found that deviations from study protocol, such as switching from an intent-to-treat analysis to a per-protocol analysis (i.e., excluding dropouts and/or those who did not adhere to treatment protocol), accounted for some of the discrepancies between the FDA and published data. Subsequent meta-analyses examined this controversy further. Fournier et al. (2010) obtained raw IPD from six of the 23 short-term RCTs of antidepressant medication (a total of 718 patients). Using IPD, these authors found that antidepressant drugs were minimally effective for patients with mild or moderate depressive symptoms (Cohen’s d = 0.11), but their effects were better for those with severe (d = 0.17) or very severe (d = 0.47) depression. The controversy regarding the efficacy of antidepressant medication illustrates that quantitative synthesis, especially utilizing IPD, can play a unique role in drawing unbiased and robust inference in treatment research.
Unfortunately, controversies like this are not limited to pharmaceutical clinical trials. A recent review of meta-analytic studies published in psychological journals also reveals a clear publication bias (Bakker, van Dijk, & Wicherts, 2012). Bakker et al. demonstrated in a simulation study that it is easier to find inflated and statistically significant effects in underpowered samples than larger and more powerful samples, especially when the true effect size is small. This may be because smaller samples capitalize on chance variations in effect sizes (Tversky & Kahneman, 1971), and also because questionable research practices (e.g., failing to report data on all outcomes) make it more likely to discover statistically significant effects. This may explain the paradox in which typical psychological studies are underpowered, yet 96% of all articles in the psychological literature report statistically significant outcomes (Bakker et al., 2012). Overall, there is evidence of generally larger effects in smaller, compared with larger, studies (Borenstein, Hedges, Higgins, & Rothstein, 2009, p. 291; see also Kraemer, Mintz, Noda, Tinklenberg, & Yesavage, 2006).
In sum, findings from single studies may not provide sufficient, unbiased evidence as to the true magnitude of the effect of an intervention and the extent to which the effect can be applied (Ioannidis, 2005). In addition, published findings in the biomedical, as well as psychological, research fields have poor replicability (Begley & Ellis, 2012; Ioannidis, 2005; Nosek, Spies, & Motyl, 2012). Given that serious negative implications are associated with such poor reproducibility, calls have been made to raise standards for clinical trials (Begley & Ellis, 2012) and psychological research in general (Simmons, Nelson, & Simonsohn, 2011), as well as to improve transparency in reporting methodology and findings (Schulz, Altman, Moher, & the CONSORT Group, 2010; Tse, Williams, & Zarin, 2009). Accordingly, integrative studies synthesizing IPD may be one promising alternative to a large-scale, multisite RCT.
Project INTEGRATE: Data and DesignProject INTEGRATE was motivated to overcome limitations of single studies and AD meta-analyses via pooling IPD from multiple college alcohol intervention trials. More specifically, Project INTEGRATE was developed to examine (a) whether BMIs are efficacious for bringing about changes in theory-based behavior targets, such as normative perceptions about peer alcohol use and the use of protective behavioral strategies while drinking; (b) whether positive changes in behavior targets predict greater reductions in alcohol use and negative consequences; (c) whether subsets of interventions are more promising; and (d) whether subgroups exist for whom different interventions are more efficacious.
The present article (a) provides a summary of the Project INTEGRATE data and its unique design characteristics; (b) describes how we established commensurate measures across studies; and (c) discusses lessons learned and offers practical recommendations for single intervention studies. Once commensurate measures across studies are established, the stated project goals can be examined using a number of appropriate analytical methods. Thus, this article does not delve into any specific analytical models, as they would depend on the research questions being examined.
A group of investigators who had published studies assessing the efficacy of BMIs for college students were contacted in the spring of 2009, asking for their willingness to contribute their deidentified data. All but one agreed, resulting in a total of 24 studies (Studies 1 through 7, 8a through 8c, and 9 through 22; see Table 1 and the online supplemental materials). Note that Studies 7 and 10 are single studies, each with two distinct subsamples. In addition to examining BMIs, all 24 studies sampled college or university students in the United States and assessed alcohol use outcome measures. Existing review studies provide some perspective about our sample of 24 studies as it relates to the body of work on college alcohol BMIs as a whole. Larimer and Cronce (2002, 2007) and Cronce and Larimer (2011) systematically searched the literature covering the combined period from 1984 to early 2010 on individual-focused preventative intervention studies, and summarized results from a combined total of 110 studies, of which approximately one third came from the last 3 years (2007 to 2010). Similarly, Carey et al. (2007) meta-analyzed data from 62 studies that focused on individual-level interventions published between 1985 and early 2007. Thus, the sample of 24 studies included in Project INTEGRATE represents a good proportion of the existing BMIs conducted between 1990 and 2009 (published between 1998 and 2010). These studies are diverse in terms of original investigators, college campuses from which participants were recruited, demographic characteristics, and intervention study designs. Our combined data set also includes data from unpublished studies (Studies 8b, 8c, and 9) and unreported data from published studies (e.g., additional cohorts; Study 20). Investigators who contributed data provided clarifications about study design and data, documentation, and intervention content for their studies.
Study Design Characteristics (N = 12,630)
Study Design Characteristics (N = 12,630)
Combined Sample
Data pooled from all 24 studies consisted of 12,630 participants. All studies included one or more BMI conditions, with the majority (21 studies) including either a control condition or other comparison condition (i.e., alcohol education). Because condition labels varied across studies, we relabeled them based on shared intervention characteristics to one of the following five categories for Project INTEGRATE (Ray et al., 2014): motivational interview plus personalized feedback (MI + PF; n = 10), stand-alone personalized feedback (PF; n = 11), group motivational interview (GMI; n = 11), alcohol education (AE; n = 6), and control (n = 19). There were three unique conditions that did not fit these categories: an MI + PF combined with an AE intervention, an MI without PF, and an MI + PF combined with a parent-based intervention (see Table S1 of the online supplemental materials for all 60 intervention groups and their new labels included in Project INTEGRATE). Participant recruitment and selection also varied across studies, ranging from volunteer students recruited with flyers to students who were required to complete an alcohol program because they violated university rules about alcohol. Although some studies (i.e., Studies 8a, 8b, 8c, 10, 20, and 22) had assessments beyond 12 months postbaseline, we decided to focus only on follow-up data up to a year, as there was a considerable lack of overlap in timing of assessments beyond this point. Each study assessed participants at least twice from baseline up to 12 months. More details on participant characteristics, assessment schedules, and classification of study conditions can be found in Table 1.
More than half of the combined sample is comprised of women (58%) and first-year or incoming students (58%). The majority of the sample is White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% belonging to other or mixed ethnic groups. Approximately 15% are college students mandated to complete a university program as a result of alcohol-related infractions, 27% are members (or pledged to be a member) of fraternities and sororities, and 13% are varsity athletes or members of club sports. Two studies of mandated students (Studies 2 and 7.1) utilized a waitlist control within the 12-month follow-up period. To preserve the original randomized group assignment at baseline, we excluded data from those control cases who were waitlisted initially at baseline and received an intervention at a time that the follow-up assessment took place for other treatment groups (i.e., 119 from Study 2 at the 6-month follow-up; 24 from Study 7.1 at the 6-month follow-up; see Table 1). The majority of the individual studies included in Project INTEGRATE have been previously described in the published literature. Additional study details that were not described previously are provided in the online supplemental materials.
In addition to this combined intervention data set, there were additional participants who were not part of the original intervention studies. Adding these screening or nonrandomized participants resulted in a total of 24,336 participants (60% women; 48% first-year or incoming students). This larger data set was used for item response theory (IRT) analysis and sensitivity analysis, as well as for research questions that did not involve intervention efficacy (e.g., racial and gender differences in alcohol-related problems; Clarke, Kim, White, Jiao, & Mun, 2013).
Study Design Characteristics and Analytic Considerations
IDA studies can be developed for specific research questions and there are a number of appropriate analytical approaches that can be utilized, depending on the nature of those questions as well as characteristics of the pooled data itself. Nonetheless, a discussion of some of the challenges and our counter measures to overcome them may be helpful for other IDA studies. The Project INTEGRATE data have a three-level data structure in which multiple repeated assessments are nested within individuals who are nested within studies. If no adjustment is made, any resulting standard error from the nested data tends to be underestimated and the power to detect any effects tends to be overestimated. This nested, correlated data structure can be measured using an intraclass correlation coefficient (ICC). Although the study ICC may be relatively small in our pooled data, the average cluster size (i.e., study sample size) is large, and the design effect, which is estimated as 1 + ICC × (average cluster size −1), can be substantial. In one analysis of a subsample, ICCs were small, ranging from 0.05 to 0.26, but the design effects were huge, ranging from 34.6 to 166.0, because of the large average cluster size (n = 648). To address this issue, we can use a sandwich variance estimator (see Hardin, 2003, for a review) suited for complex survey data. In conjunction with complex survey analysis, we can weight or scale data at the individual level (e.g., by using a weight of 1 over the square root of the sample size of each study) to prevent large studies from exerting overly dominating influences on overall estimates (see Table 1 for discrepant sample sizes across studies). In principle, large studies contribute more information and should count more toward estimates. However, a weighting strategy like this places slightly higher value on individuals’ information from smaller studies relative to individuals’ information from larger studies. An alternative approach is to utilize the multilevel modeling framework using either fixed-effects or random-effects models, which weight data differently when combining effects across studies (see DerSimonian & Laird, 1986) due to different assumptions involved in each modeling approach. This multilevel modeling approach can also accommodate weights, although the best practice may differ for each research project. Both complex survey analysis and multilevel analysis can readily be implemented by using commercially available software programs.
Project INTEGRATE: MeasuresOne of the most important challenges in conducting IDA or meta-analysis using IPD is to ensure that measures are comparable across studies (Cooper & Patall, 2009; Curran & Hussong, 2009; Hussong, Curran, & Bauer, 2013). To address this issue, we utilized harmonization and developed innovative IRT models. Table S2 of the online supplemental materials provides a list of our key constructs and overlap across studies, as well as the approach taken for each construct. For IRT analysis, some harmonization steps were taken to find common response options or to derive items that could be collapsed and linked across studies. Note that the overlap in measures across studies was excellent at the level of construct, but not at the item level. Within each study, most of the conceptual mediator variables were assessed at the same time as outcome measures.
Hierarchical IRT Approaches
When a construct was assessed using multiple items or scales that are well established in the literature, and when there was a subset of construct items that could be linked across studies, we conducted IRT analysis to obtain commensurate measures across studies. IRT, or latent trait theory (Lazarsfeld & Henry, 1968; Lord & Novick, 1968), has been used extensively in the area of educational testing and measurement, and with increasing frequency in psychological research (e.g., Gibbons et al., 2012). Unlike classical test theory, in the IRT framework, item parameters are independent of parameters describing individuals (or studies), which is a critical advantage for the current project, for which item subsets vary by individual and by study. Given the unique qualities of the Project INTEGRATE data, existing IRT methods were extended to handle sparse data, take into account study-level information (e.g., different trait means across studies), and borrow information, when possible, from related or higher order dimensions. More specifically, we developed several IRT models adapted from hierarchical, multiunidimensional, as well as unidimensional two-parameter logistic IRT (2-PL IRT) models, and developed MCMC algorithms to fit these IRT models within a hierarchical Bayesian perspective. Huo et al. (2014) provides the theoretical and technical details of the 2-PL IRT models and MCMC algorithms, as well as the findings of two simulation studies and real data analysis. The MCMC codes were written in Ox (Doornik, 2009), a matrix-based, object-oriented programming language, and are available upon request.
Scoring of latent trait scores across time
For each construct, item parameters were calibrated using baseline data, and these calibrated item parameters were then used to estimate latent trait scores for baseline and subsequent follow-up data. Prior to longitudinal scoring, we checked whether different items were assessed at different time points, and whether different sets of items used at different time points could have introduced bias in our estimation of latent trait scores. Furthermore, not all individuals assessed at baseline were followed up, either by study design or due to attrition. Therefore, we conducted sensitivity analyses by recalibrating data using different sets of items and different subsets of participants across time. We compared the descriptive statistics (e.g., means and standard deviations) of the estimated item parameters, structural parameters, and trait scores by using different sets of items calibrated and checked their correlations (r = .99), which led us to conclude that the differences in items and participants over time did not exert any meaningful influence on our estimates. Next, we give examples of how latent trait scores—often called “theta (θ) scores” in IRT—were established for two key constructs.
Alcohol-related problems
A total of 71 individual items were assessed in all 24 studies. Of the 71 items, three pairs of very similarly worded items were combined (e.g., “I have become very rude, obnoxious, or insulting after drinking”; “Have you become very rude, obnoxious, or insulting after drinking?”) and 68 unique items were subsequently analyzed. Items came from the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989), the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher, 1992), the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ; Kahler, Strong, & Read, 2005), the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993), the Positive and Negative Consequences Experienced questionnaire (D’Amico & Fromme, 1997), and the Alcohol Dependence Scale (Skinner & Allen, 1982; Skinner & Horn, 1984). For each item, responses were dichotomized to indicate 1 = “yes” and 0 = “no,” because this response format was the common denominator across studies. When someone did not drink during the time frame referenced, their score was recoded as zero.
Several existing psychometric studies on alcohol-related problems have used a single-factor structure (RAPI: Neal, Corbin, & Fromme, 2006; YAAPST: Kahler, Strong, Read, Palfai, & Wood, 2004; BYAACQ: Kahler et al., 2005; Diagnostic and Statistical Manual of Mental Disorders (4th ed.): American Psychiatric Association, 1994; alcohol use disorder symptoms: Martin, Chung, Kirisci, & Langenbucher, 2006). Thus, we derived latent trait scores using a unidimensional 2-PL IRT model, which assumes that a single overall severity latent trait gives rise to item responses. We also estimated a four-dimensional 2-PL IRT model (the four related, but distinct, dimensions were Neglecting Responsibilities, Interpersonal Difficulties, Dependence-Like Symptoms, and Acute Heavy Drinking). The estimated correlations among the four dimensions exceeded 0.8. For two small studies (Studies 13 and 14; combined N = 138), only sum scores of the RAPI, but not individual item scores, were available. We matched latent trait scores for these participants using their RAPI sum scores with those from studies that had both latent trait scores and RAPI sum scores.
In the factor analysis environment, items are evaluated in terms of their factor loadings and thresholds (intercepts for continuous indicators), whereas in IRT analysis, items are typically evaluated by their discrimination and difficulty (or severity) item parameters. The item discrimination parameter is the slope of the item characteristic curve that indicates an item’s ability to discriminate among respondents, and how strongly an item is correlated to the underlying latent trait. Items with steeper slopes indicate better discrimination. For example, Item C (“The quality of work suffered because of drinking”) in Figure 1 discriminates respondents better than Item E (“Getting into trouble because of drinking at work or school”). The item difficulty parameter indicates the location of the item along the latent trait continuum where the probability of endorsement of the item is 0.5, and indicates how easy or difficult the item is to endorse. Items with higher difficulty are less frequently endorsed. We examined the total information curve (see Figure S1 in the online supplemental materials), which provides the overall performance of the measure at each level of an underlying latent trait (Markon, 2013). Overall, the items for alcohol-related problems provided less reliable or precise information about individuals whose underlying latent traits were at the lower end of the spectrum, but more reliable information for individuals whose traits were at the higher end of the spectrum (e.g., θ scores between 1 and 3). This also reflects that few alcohol-related problems items are easy to endorse in the present study, and that the majority of these items are more sensitive and informative for those who report high levels of alcohol-related problems, which is similar to findings from a previous analysis (Neal et al., 2006).
Figure 1. Item characteristic curves of several items in a two-parameter logistic item response theory model. A = “While drinking, I have said or done embarrassing things”; B = “Said things while drinking that you later regretted”; C = “The quality of my work or school work has suffered”; D = “Told by a friend or neighbor to stop or cut down on drinking”; E = “Gotten into trouble at work or school”; F = “Almost constantly think about drinking alcohol.” Numbers in parenthesis indicate item discrimination and severity parameters, respectively. See the online article for the color version of this figure.
It is worth mentioning that in deriving latent trait scores, there was a need to reconcile different referent time frames across studies. Most of the studies used a short referent time frame (3 months or shorter) for alcohol-related problems at baseline. More specifically, 20 studies out of 24 used a 1- to 3-month time frame, and three studies (Studies 4, 10, and 12) used a 6-month time frame. Only one study (Study 3) measured past-year alcohol-related problems using the YAAPST items and also included the AUDIT items, which ask about the last year (see Table S3 of the online supplemental materials for measure overlap and referent time frames at baseline across studies). A few studies asked about problems that occurred in two or three different referent time frames, and we examined their responses. Study 20, in particular, had 1-month, 6-month, and 1-year referent time frames for each RAPI item. Because there is a part–whole relationship between answers for the 1-month time frame and answers for the 6-month time frame, item endorsement rates should be higher for items assessed over the longer time frame. However, the differences across the three time frames were relatively small in magnitude, and also depended on item characteristics. For example, for a relatively easy item to endorse, such as “Got into fights, acted bad, or did mean things,” endorsement rates went up progressively across time frames (i.e., 15%, 23%, and 28%, respectively). For a relatively more difficult or severe item, such as “Felt that you had a problem with alcohol,” endorsement rates tended to be stable regardless of the referent time frame (i.e., 8%, 10%, and 11%, respectively). Correlations between 1-month and 6-month responses were also high (0.78 for the easy item; and 0.90 for the more difficult item). Most of the studies had a 1- to 6-month referent time frame at baseline, and follow-up assessments utilized a 1- to 3-month time frame in all studies. Note also that through IRT analysis, the measurement perspective was changed from the number of alcohol-related problems that occurred within a given time frame (i.e., a count variable) to the severity of alcohol-related problems (i.e., a trait score in a normal distribution).
The correlations between the original scale sum scores (e.g., the RAPI or YAAPST sum scores) and latent trait scores within studies were, on average, 0.83, suggesting that the rank orders of individuals within studies were similar across the two approaches (i.e., summed scale scores and theta scores from the IRT analysis). However, these two approaches are based on different measurement models and items, and are not directly comparable.
Protective behavioral strategies
Protective behavioral strategies refer to specific cognitive–behavioral strategies that can be employed to reduce risky drinking prior to and during drinking, and to limit harm from drinking (Martens et al., 2005). A total of 58 protective behavioral strategy items assessed by 13 studies were analyzed. Items came from the 10-item Protective Behavioral Strategies (PBS) measure taken from the National College Health Assessment survey (American College Health Association, 2001), the 15-item Protective Behavioral Strategies Scale (PBSS; Martens et al., 2005), the 37-item Self-Control Strategies Questionnaire (SCSQ; Werch & Gorman, 1988), a seven-item Drinking Restraint Strategies scale used in Wood, Capone, Laforge, Erickson, and Brand (2007), and a nine-item Drinking Strategies (DS) scale reported in Wood et al. (2010). We removed items that indicated either abstinence (i.e., “Chose not to drink alcohol”) or risky (as opposed to protective) drinking behaviors (i.e., “Drink shots of liquor”), as well as items that were used in only one study, as they could not be linked to measures of other studies for our IRT analysis. Of the remaining items, 20 were combined into five individual items because they were very similarly worded (e.g., “use a designated driver”; “used a designated driver”; “use a safe ride or taxi service when you have been drinking”; “make arrangements not to drive when drinking”). Forty-three remaining items were analyzed via hierarchical IRT, specifying a single, underlying dimension of protective behavioral strategies. Although the literature varies as to the dimensionality of these behaviors (e.g., three dimensions for the PBSS in Martens et al., 2005; four dimensions for the PBSS in Walters, Roudsari, Vader, & Harris, 2007; seven factors for the external SCSQ in Werch & Gorman, 1988; one summed score for the DS in Wood et al., 2010), we used a unidimensional IRT model because of lack of overlap in items across studies and also because protective behavioral strategies are often used as a single overall score (e.g., Benton, Downey, Glider, & Benton, 2008; Martens, Ferrier, & Cimini, 2007). Furthermore, the three dimension scores of the PBSS are similarly related to alcohol use, alcohol-related problems, and depressive symptom scores (Martens et al., 2005; Martens et al., 2008). Only data for individuals who reported recent drinking (i.e., past 1 to 3 months) were included.
Items were recoded to indicate 0 = never; 1 = rarely, seldom, occasionally, or sometimes; 2 = usually or often; and 3 = always. Although some of the past studies dichotomized item responses (0 and 1 vs. 2 and 3; e.g., Walters, Roudsari, et al., 2007), we deemed it important that a protective behavior that is often used be differentiated from one that is always used, and that this difference be reflected in estimating latent trait scores. Thus, we used a generalized partial credit model (Muraki, 1992) to assign partial credit for polytomous items. Unlike the previous IRT model, the single difficulty parameter of an item is replaced by three step difficulty parameters, each of which can be interpreted as the intersection point of two adjacent item response curves (0 and 1, 1 and 2, 2 and 3; see Figure 2). These intersection points are the points on the latent trait scale axis (x-axis), in which one response (e.g., 2 = usually or often) becomes relatively more likely than the preceding response (e.g., 1 = rarely).
Figure 2. Category response curves of two items (Figure 2A and Figure 2B) of protective behavioral strategies from the generalized partial credit item response theory model. Item A (“Stop drinking at a predetermined time”) in Figure 2A had a slope parameter of 0.99, with item step difficulty parameters of −0.69 (from 0 to 1), 1.62 (from 1 to 2), and 2.24 (from 2 to 3), respectively. Item B (“Eat before and/or during drinking”) in Figure 2B had a slope parameter of 0.49, with item step difficulty parameters of −3.85 (from 0 to 1), −0.48 (from 1 to 2), and 1.29 (from 2 to 3), respectively. See the online article for the color version of this figure.
Figure 2 shows category response curves for two protective behaviors under the partial credit model. It is relatively easy for participants to endorse “rarely” or “usually” as opposed to “never” for Item B (“Eat before and/or during drinking”), compared with Item A (“Stop drinking at a predetermined time”). Most of the responses to Item A were either “never” or “rarely.” In contrast, most of the responses to Item B occurred between “rarely” and “always.” Item step difficulty parameter estimates reflect this relative difficulty. Item step difficulty parameter estimates for Item A were higher than those for Item B at intersection points (e.g., 1.62 vs. −0.48 for Item A vs. Item B for the intersection between “rarely” and “usually”). In sum, it is relatively more difficult to stop drinking at a predetermined time than to eat food during or before drinking. Polytomous items, therefore, can meaningfully be interpreted in terms of how difficult one item is to endorse compared with other items. The correlations between the original scale sum scores (e.g., the PBS, PBSS) and latent trait scores within studies exceeded 0.96.
Differential item functioning (DIF) and latent traits
DIF tests examine whether participants with the same level of a construct but different backgrounds respond similarly to the same items, and are often conducted in IDA research (Curran et al., 2008; Hussong et al., 2007). Likewise, important covariates, which can be different for different IDA studies, can be included in measurement models when estimating latent traits (e.g., moderated nonlinear factor analysis; Bauer & Hussong, 2009; Curran et al., 2014). Each IDA study may also make certain assumptions about data and item performance.
In deriving item parameters and latent trait scores in the current study, we initially made an assumption that the same items administered in different studies had the same item parameters as specified in the item response function, after taking into account different average trait levels across studies. We reasoned that it is a sensible assumption because all participants were college students who were assessed within a narrow window of assessment (i.e., 12 months). In addition, we had a high proportion of overall missing data at the item level, which can be attributed to the large number of both studies and items that were pooled. Note also that we treated many similarly worded items as different items, which increased the number of items and, consequently, the amount of missing data. The high proportion of missing data for this large-scale IDA data set made it very difficult, if not impossible, to examine DIF for many items (see Huo et al., 2014, for an example of item overlap across studies [in their Table 5] and findings from a simulation study on missing data). In addition, the amount of missingness prevented us from using existing software programs, such as Mplus (Muthén & Muthén, 1998–2014), to compute the tetrachoric correlation from which further analyses (e.g., factor analysis, structural equation modeling analysis) can be conducted.
We should note that there exists an indeterminacy between DIF and group (study) differences in latent traits, which has been well known among psychometricians for some time (e.g., Thissen, Steinberg, & Gerrard, 1986), and that DIF depends on the items or a set of items that serve as a point of reference (i.e., an anchor) because the choice of invariant items within a pool of items affects how remaining items behave (Bechger, Gunter, & Verstralen, 2010; see also Byrne, Shavelson, & Muthén, 1989, for the nonindependence of these tests in the context of confirmative factor analysis). That is the reason why DIF items within a set of items can change depending on search strategies and measurement models (Kim & Yoon, 2011; Yoon & Millsap, 2007). In other words, DIF is only relative to the reference point, which can be set in several ways in a large pool of items. Consequently, latent trait scores can also shift up and down along the theta scale depending on the invariant items or DIF items, although relative positions of individuals on this scale may remain the same across groups. Even when DIF items exist across groups within a pool of items, as long as there are invariant anchor items that provide linkage across groups, latent trait scores can reasonably be estimated. Some in the literature have stated that only one invariant (i.e., non-DIF) item is necessary to establish partial invariance across different groups (studies) for a single unidimensional construct (e.g., Steenkamp & Baumgartner, 1998; also briefly noted in Bauer & Hussong, 2009). Due to this nature of DIF, it may not be best to focus on which items show DIF.
What is central for IDA is whether trait scores are unbiased in relation to a key design variable (i.e., study). Thus, we conducted an additional IRT analyses for alcohol-related problems to examine this question. We compared the latent trait scores from the original IRT model (no-DIF model) with those from alternative models that subset a portion of items to take different item parameters (i.e., DIF items) across studies. If latent trait scores resulting from these different approaches (i.e., no-DIF model vs. DIF models) are equivalent, we can be assured that our trait scores, as a whole, are invariant to study. Our strategy is essentially equivalent to the IRT strategy adopted by Hussong et al. (2007), with the exception that in Hussong et al., DIF items were specifically specified, whereas we allowed some items to have DIF across studies. Results indicated that no meaningful differences existed in latent trait scores between these two IRT approaches (see Figures S2 and S3 in the online supplemental materials). The rank orders of individuals within and across studies were preserved across the two IRT models (rs ≥ 0.95). In addition, the rank orders of the studies in terms of their observed theta means were also largely the same. However, our original no-DIF model was the simpler, more parsimonious model, and had the lower deviance information criterion (DIC; Spiegelhalter, Best, Carlin, & van der Linde, 2002) than the alternative DIF model. The DIC is appropriate for comparing models that are estimated from MCMC analysis. It can be considered as a generalized version of the Akaike information criterion (Akaike, 1974) and Bayesian information criterion (Schwarz, 1978) for Bayesian models. Thus, through the use of novel IRT methods, we were able to combine different items from different scales across the studies included in Project INTEGRATE. The result of these IRT analyses is that all participants could be placed on the same underlying trait scale, although these traits were assessed with different scales, items, and/or response options in the original studies.
Given that we have intervention and control groups, we also checked latent trait scores obtained from our IRT analysis to make sure no systematic bias exists in separating these groups within studies. With the exception of the three studies that did not have a control group, all individual studies utilized random assignment. Thus, the treatment and control groups should be, and were, mostly equivalent at baseline when comparing either original scale scores or latent trait scores. Table S4 of the online supplemental materials provides a list of important considerations and actions that we have made to estimate latent trait scores.
Harmonization
Harmonization can be described as the recoding of variables so that values from different variables assessing the same construct can be made comparable. More broadly, harmonization refers to a general approach in which measures are retrospectively made comparable to synthesize large data sets, and it is increasingly utilized in biomedical epidemiological research (e.g., Fortier et al., 2010). Harmonization can be straightforward if standard measures, such as the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985), are utilized. The DDQ asks respondents to indicate the number of drinks they consumed on each day of a typical week in the last month. In the present study, the majority of studies utilized the DDQ, which allowed us to create several key alcohol use frequency and quantity measures. Although the usual time frame for the DDQ is past month, a few studies utilized the past 3 months as a referent time frame. We assumed that this different time frame does not bias the self-reported number of drinks consumed on each day of a typical drinking week for college students.
Trade-off between item overlap and information
In the absence of standard measures, however, one needs to weigh a gain in item overlap across studies against a loss of information that can result when trying to find a common denominator for items. In our research, for example, three studies assessed the number of drinking days (frequency of alcohol use) in the past month as an open-ended question, and six studies collected daily drinking diaries for a 30-day window, which could then be used to compute the number of drinking days in the past month. In contrast, six other studies assessed the frequency of drinking using the AUDIT, which had the following ordinal response options: 0 = never; 1 = monthly or less; 2 = 2–4 times a month; 3 = 2–3 times a week; and 4 = 4 or more times a week. In this case, the AUDIT ordinal response format provides the “lowest common denominator” among response options, but using it would lead to a loss of information for those studies that used more detailed assessments. Thus, deriving a comparable measure using harmonization required striking an appropriate balance between item overlap across studies and information (i.e., greater overlap but loss of information vs. more information retained for fewer studies). For many of the secondary outcome measures, we derived dichotomous outcome measures (e.g., any driving after three drinks or more in the past year) to ensure the broadest possible measurement coverage across studies.
Limits of harmonization
For some constructs, it was not possible to derive a common measure that could be meaningfully compared across studies. One such example was heavy episodic drinking, a well-known and widely used outcome measure in studies of college student drinking. Heavy episodic drinking, or binge drinking, is defined by the National Institute on Alcohol Abuse and Alcoholism (2004) as a pattern of drinking alcohol that brings blood alcohol concentration to 0.08 g percent or above, which corresponds to consuming five or more drinks (men) or four or more drinks (women) in 2 hours. Questions actually used in studies were (a) “How many times have you drank 5 drinks or more?” (for men; 4 or more drinks for women); (b) “How many times have you drank 5 drinks or more?” (i.e., regardless of sex); (c) “How many times have you drank 6 drinks or more?” (i.e., regardless of sex); (d) “How many times have you drank 5 drinks or more in one sitting?” (or in a row); and (e) “How many times have you drank 5 drinks or more within two hours?” These questions were also asked for different referent time frames: in the past 2 weeks, 4 weeks, or 1 month. Thus, key differences across all items were the referent time period (2 weeks vs. 4 weeks/1 month), number of drinks (four, five, or six drinks), sex (sex-specific vs. sex-nonspecific), and duration of a drinking episode (unspecified hours, one sitting or in a row, or two hours). Two studies assessed both 2-week and 1-month heavy episodic drinking measures (five or more drinks for men and four or more drinks for women). Examining means and correlations of these items for these studies led us to conclude that heavy episodic drinking in the past 2 weeks could not be multiplied by 2 to create a measure for the past month (see Table 2). Although these two measures were highly correlated (r > .7), their means were more difficult to map onto a common metric. The 1-month question tended to be an underestimate of the 2-week measure that was multiplied by 2, except in one case in which it was overestimated (i.e., women in Study 22). Thus, any between-study differences in heavy episodic drinking would be confounded with the way heavy episodic drinking was assessed.
Heavy Episodic Drinking (HED) Variable as an Example When Harmonization Was Not Feasible
Similar to the heavy episodic drinking measure, we concluded that readiness to change, a key mediator variable, could not be made comparable across studies. Readiness to change refers to the degree to which an individual is motivated to change problematic drinking patterns, and is measured by assessing different stages of cognitive and affective processes that lead to an initial change effort (Carey, Purnine, Maisto, & Carey, 1999). Although this construct was measured in the majority of the studies, each study included only one scale or a single item assessing this construct, and there was little overlap across the studies. Eight studies used the Readiness to Change Questionnaire (Heather, Rollnick, & Bell, 1993); four studies used the University of Rhode Island Change Assessment (Heesch, Velasquez, & von Sternberg, 2005); and seven studies used different variations of a single-item, readiness to change ruler (LaBrie, Quinlan, Schiffman, & Earleywine, 2005) or contemplation ladder (Herzog, Abrams, Emmons, & Linnan, 2000) that differed in response ranges (1 to 5, 1 to 10, or 0 to 10), as well as anchor points to mark different stages of change. With just one measure for each study and with no overlap in items across scales (and studies), we concluded that any differences in measures would be confounded with between-study differences (e.g., sample or design characteristics). Thus, any analyses using “readiness to change” will have to be replicated across the different scales that are available rather than using the pooled data set. The steps taken and outcomes from these steps thus far demonstrate that even with latest advances in analytical modeling and well-established measures for key constructs, there are some limits. In the next section, we provide a discussion of how to better design individual studies, especially intervention studies, with IDA in mind.
Lessons Learned Thus Far and RecommendationsOne of the most striking lessons that we have learned thus far is that this innovative approach to synthesizing information from multiple studies is very labor-intensive and time-consuming. To meaningfully conduct IDA studies for clinical outcomes, such as intervention efficacy and moderated efficacy, the number of studies included should be sufficiently large to examine study-level, as well as individual-level, differences. However, IDA demands significant time and resources to pool data from studies, clean and check data, and establish commensurate measurement scales. Citing the work by Steinberg et al. (1997) and personal communication with one of the investigators, Cooper and Patall (2009) noted that IPD meta-analysis probably costs 5 to 8 times more than AD meta-analysis, and takes several years from start to publication in the field of medical research. When standard measures are less commonly used or difficult to establish across studies, which is typical for psychological research, the cost may be even greater than what has been estimated for medical research, in which the primary outcome (e.g., death) can often be clearly defined.
For Project INTEGRATE, we developed new MCMC algorithms to estimate item parameters and latent trait scores across studies, which took an enormous amount of time and effort, because commercially available software programs did not sufficiently meet our needs. Our first-hand experience suggests that the application of IPD meta-analysis may require further methodological developments, whereas AD meta-analysis procedures are fairly well-established at the present time. Furthermore, unlike in medical trials in which treatment and control conditions can clearly be defined (i.e., a specific procedure or drug), we learned that treatment and control groups may not be equivalent across studies, which required a closer examination of these groups to ensure that similarly labeled groups in original studies had many critical features in common (Ray et al., 2014).
The capabilities to reexamine effect sizes using more appropriate analytical methods and to peruse intervention procedures to appropriately compare treatment groups are important advantages of IDA over single studies or AD meta-analysis. In the context of IPD meta-analysis, multiple RCTs are typically conceptualized as a sample of studies. The findings can then be generalized to a broader population. A recent IPD meta-analysis study that examined the efficacy of BMIs for Project INTEGRATE is one such research application (Huh et al., 2014). In Huh et al., we utilized Bayesian multilevel, overdispersed Poisson hurdle models to examine intervention effects on drinks per week and peak drinking, and Gaussian models for alcohol problems. This analytic approach accommodated the sampling, sample characteristics, and distributions of the pooled data while overcoming some of the challenges associated with being an IDA study, one of which was the unbalanced RCT design (i.e., 21 interventions vs. 17 controls across 17 studies) of the pooled data set. Although the study by Huh et al. highlights some of the promises of IDA, for this type of investigation, a large enough number of studies are needed to obtain sufficient precision about point estimates and standard errors. Others have said that at least 10 to 20 studies may be needed for population representation and proper model estimation (e.g., Hussong et al., 2013). As the number of studies included for IDA goes up, however, so does the demand on time and other resources.
Having emphasized the need for greater resources for IDA, we remain enthusiastic that IDA is a better research strategy for examining low-base-rate behaviors, such as marijuana or drug use outcomes (White et al., 2014), and for finding subgroups who may respond to treatment differently (i.e., moderators of treatment outcomes), which is widely considered as one of the most important strengths of IDA (e.g., Brown et al., 2013). Thus, IDA holds special promises for the field. We would also like to note that the resources needed may be highly specific to the research goals of individual IDA studies. Other notable strengths of IDA, compared with single studies, include larger, more heterogeneous samples and more repeated measures for longer observed periods. Depending on the specific research questions, the pooled data set from just two studies may be better than data from a single study, as long as the replicability of measurement models can hold across studies.
Emerging analytical and technological advances may provide more favorable environments for pooling and analyzing IPD in the future. In the present moment, our experiences suggest ways to lower barriers to IDA by planning single intervention studies differently. We have several recommendations for future single intervention studies.
Increase Overlap in Measures
The simplest option to increase overlap in measures across studies is to use standardized and common measures for a given construct in future single studies. If there is a need to include a newly developed questionnaire or instrument, it would be quite helpful to include other established measures of the same construct to link items from different measures. Note that the overlap needs to exist not just at the level of the constructs but also at the level of items (and response options). When a concern arises about burdening participants with multiple items, it may be better to administer a portion of items from one measure and a portion of items from other measures (e.g., two versions, A-B and B-C, administered to two groups), as is done in a planned missingness design (Graham, Hofer, & MacKinnon, 1996). This strategy, a common practice in educational research, is better for IDA because items can be linked across studies. In theory, a single item may be used to provide such a chain. However, the level of precision or trustworthiness of the chain will improve with more shared items across studies. Our experience also suggests that, with more work, item banks may be developed for key constructs for this college population, which may make it feasible to derive latent trait scores across studies in the future without the needed overlap in items. At present, there is no such known item bank specifically aimed at this population.
Based on our experience, the importance of common, standard items may be greater for single-item measures, such as heavy episodic drinking, which are often utilized in alcohol research. Our experience is by no means unique. Other investigators have also noted the difficulty of harmonizing alcohol measures across studies (e.g., analysis of twin studies: Agrawal et al., 2012; genome-wide association studies: Hamilton et al., 2011). Future investigations could utilize measures from well-researched and accessible research tools, such as the Phenotypes and eXposures (PhenX) Toolkit (Hamilton et al., 2011; https://www.Phenxtoolkit.org/), the NIH Toolbox for assessment of neurological and behavioral function (http://www.nihtoolbox.org/Pages/default.aspx), or the Patient-Reported Outcomes Measurement Information System (PROMIS; http://www.nihpromis.org/; see Pilkonis et al., 2013, for the development of item banks for alcohol use, consequences, and expectancies).
Increase Overlap in Follow-Ups
The ability to extend the range of observations in terms of the observed time period is one of the advantages of IDA over single studies. However, this can lead to a greater portion of missing data in the combined data set. Two types of missing data exist in IDA: items that were not assessed by study design, and are thus missing at the level of studies; and items that were included but not answered by the participant (Gelman, King, & Liu, 1998). Table 1 provides a glimpse of the sparse nature of pooled data across time, especially at longer-term follow-ups (e.g., 6 to 12 months postintervention). Table S2 of the online supplemental materials shows available constructs for each study. In both tables, missing data are due to different study designs across studies. Within studies, there were also missing data at the individual level due to omitted responses. Although missing values may be random in nature (i.e., missing at random [MAR]) and ignorable (Schafer, 1997) for this project, the pattern of missingness was unique for some studies, and the overall proportion of missing data was substantial.
The missing data challenge can be mitigated if there is better overlap in follow-up assessments across intervention studies. Overall, the power to detect a group difference goes up with increases in the duration of observations, the number of repeated assessments, and the sample size. Of those, the duration has the greatest effect, and the number of repeated assessments has the smallest effect, on power (Moerbeek, 2008). Despite the small effect on power, to capture a change immediately following an intervention and a slower subsequent rebound, one has to have at least four (preferably more) repeated assessments to estimate polynomial growth models without the need to impose restrictive constraints. Although it is reasonable to assess outcomes more frequently, for example, in the first 3 months following a BMI, it is also desirable to assess outcome data beyond the initial phase to see whether, and for how long, the intervention effect is sustained. We recommend that future alcohol intervention trials extend the period under observation to intermediate or long-term follow-ups (e.g., 6 to 12 months postintervention), as this longer-term follow-up is needed from both substantive and methodological perspectives. Assuming that missing data at follow-ups (i.e., dropouts) meet the MAR assumption, the extension of the observed duration should improve power to detect intervention efficacy and other mediational effects. Similar to the case of increasing item overlap by design, the use of planned missingness may prove to be useful in estimating patterns of change for intervention studies. A design of 1-, 3-, 6-, and 9-month follow-ups for a random half sample, and 1-, 2-, 6-, and 12-month follow-ups for the other half, for example, would provide up to seven time points, including baseline, for up to a year, with overlap at baseline, 1-month follow-up, and 6-month follow-up.
Reduce Heterogeneity in Treatment and Control Groups Across Trials
Project INTEGRATE includes interventions that varied, for example, in the number and type of content topics covered and the manner in which they were delivered (e.g., in-person one-on-one, in-person group, by mail) to participants across studies. Therefore, we developed detailed coding procedures for all intervention and control conditions, which allowed us to determine whether similarly labeled groups are indeed equivalent (see Ray et al., 2014, for detail). Based on the content analysis of these components across conditions and the subsequent analysis of those components, we relabeled some of the groups and removed others from the main data set (see Table S1 of the online supplemental materials). This observation highlights a need to develop detailed documentation on the proposed mechanisms and protocols for any new treatment and for any new variant of an existing, evidence-based treatment in the future. In designing future single studies, one should also carefully consider a treatment group and a comparison group for their comparability and overlap with other studies.
Improve Transparency and Documentation
In general, it would be helpful to have greater transparency and better documentation in published articles, as well as in unpublished supporting materials. General reporting guidelines, such as the CONSORT statement (Schulz et al., 2010) and the Journal Article Reporting Standards by the APA Publications and Communications Board Working Group on Journal Article Reporting Standards (2008), have provided a minimum reporting standard for various types of studies, including RCTs. ClinicalTrials.gov, an online registry and results database for Phases 2 through 4 intervention studies, provides easy access to some of the critical, scientific information about clinical studies (i.e., participant flow, baseline characteristics, outcome measures, statistical analyses, and adverse events; Tse et al., 2009). However, the required minimum information for ClinicalTrials.gov focuses on the overall efficacy and adverse events of a treatment, and does not go far enough to facilitate future IDA investigations.
We recommend that any additional outcome measures and covariates at each assessment point, follow-up schedules (beyond posttreatment), and any additional groups (treatment arms) be publicly accessible if they are omitted in published articles. This supplementary information, which could be publicly accessible and searchable, would facilitate IDA studies in the future by helping to select studies for IDA or determining feasibility of such investigations. More detailed and accurate documentation will decrease the need, for example, to pore over codebooks, questionnaires, and data to examine the nature of variation in key outcome measures and covariates. Making this information publicly available may also help to increase awareness among investigators as to the potential overlap with other studies when planning a single study.
ConclusionsProject INTEGRATE was launched to generate robust statistical inference on the efficacy of BMIs for college students, and to examine theory-supported mechanisms of behavior change. The detailed account outlined in this article illustrates both the promises and challenges of this particular IDA project and of IDA in general. The promises of IDA are attractive in the current research environment, in which limited resources are maximized by taking advantage of more efficient designs and analyses. Moreover, IDA investigations are well positioned to confront current outcries about replication failures and potentially overstated treatment benefits in the era of evidence-based-treatment decision making. At the same time, these notable promises are coupled with significant challenges. IDA is not a single analytic technique per se. Rather, it is a set of advanced methods that can be tailored and implemented to address specific goals and challenges of each IDA study, which can be seen clearly in the present article. Our strategies and procedures differed from those of others (e.g., Hussong et al., 2013), which can be attributed to the different data characteristics and different assumptions made about item performance in our study. More methodological research is needed to test these assumptions and to develop guidelines for IDA research, which is expected to increase in the future. Nonetheless, the specific recommendations that we have for single intervention studies may be helpful not only for more robust research practice but also for large-scale research synthesis, such as IPD meta-analysis and IDA.
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Submitted: February 20, 2014 Revised: November 8, 2014 Accepted: November 10, 2014
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Source: Psychology of Addictive Behaviors. Vol. 29. (1), Mar, 2015 pp. 34-48)
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Record: 49- Providing intensive addiction/housing case management to homeless veterans enrolled in addictions treatment: A randomized controlled trial. Malte, Carol A.; Cox, Koriann; Saxon, Andrew J.; Psychology of Addictive Behaviors, Vol 31(3), May, 2017 pp. 231-241. Publisher: American Psychological Association; [Journal Article] Abstract: This study sought to determine whether homeless veterans entering Veterans Affairs (VA) substance use treatment randomized to intensive addiction/housing case management (AHCM) had improved housing, substance use, mental health, and functional outcomes and lower acute health care utilization, compared to a housing support group (HSG) control. Homeless veterans (n = 181) entering outpatient VA substance use treatment were randomized to AHCM and HSG and received treatment for 12 months. AHCM provided individualized housing, substance use and mental health case management, life skills training, and community outreach. The control condition was a weekly drop-in housing support group. Adjusted longitudinal analyses compared groups on baseline to month 12 change in percentage of days housed and functional status, substance use, and mental health outcomes (36-Item Short-Form Health Survey; Addiction Severity Index [ASI]). Negative binomial regression models compared groups on health care utilization. Both conditions significantly increased percentage of days housed, with no differences detected between conditions. In total, 74 (81.3%) AHCM and 64 (71.1%) HSG participants entered long-term housing (odds ratio = 1.9, 95% confidence interval [0.9, 4.0], p = .088). HSG participants experienced a greater decrease in emergency department visits than AHCM (p = .037), whereas AHCM participants remained in substance use treatment 52.7 days longer (p = .005) and had greater study treatment participation (p < .001) than HSG. ASI alcohol composite scores improved more for HSG than AHCM (p = .006), and both conditions improved on ASI drug and psychiatric scores and alcohol/drug abstinence. AHCM did not demonstrate overarching benefits beyond standard VA housing and substance use care. For those veterans not entering or losing long-term housing, different approaches to outreach and ongoing intervention are required. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Record: 50- Psychometric properties of the Short Inventory of Problems (SIP) with adjudicated DUI intervention participants. Morse, David T.; Robertson, Angela A.; Psychology of Addictive Behaviors, Vol 31(1), Feb, 2017 pp. 110-116. Publisher: American Psychological Association; [Journal Article] Abstract: We used responses of two large samples of court-ordered participants from a statewide alcohol/driving safety program to investigate factor structure, score reliability, and criterion-related validity of the Short Inventory of Problems (SIP). Exploratory and confirmatory factor analyses, using both item-level and subscore-level data, support a one-factor structure for the SIP. Internal consistency score reliability estimates were consistent across samples and high enough to warrant use for making decisions about individuals. Item response theory model calibration of the scale, using a two-parameter logistic model, yielded consistent estimates of location and discrimination (slope) across samples. Estimated scale scores correlated moderately with an independent indicator of alcohol problems and poorly with an indicator of risky driving behavior, lending evidence of convergent and discriminant validity. We judge the SIP as adequately described by a single factor, that the joint person-item scale is coherent, and scores behave consistently across samples. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Psychometric Properties of the Short Inventory of Problems (SIP) With Adjudicated DUI Intervention Participants / BRIEF REPORT
By: David T. Morse
Department of Counseling, Educational Psychology, and Foundations, Mississippi State University;
Angela A. Robertson
Social Science Research Center, Mississippi State University
Acknowledgement:
The Short Inventory of Problems (SIP) measure comprises 15 items from the 50-item (45 scored; 5 control) Drinker Inventory of Consequences (DrInC; Miller, Tonigan, & Longabaugh, 1995). The DrInC was distinctive in focusing on adverse consequences of drinking rather than on drinking behavior or alcohol dependence. Five rationally identified domains of consequence are included: physical (8 items), intrapersonal (8), social responsibility (7), interpersonal (10), and impulse control (12). Five control items are also embedded, as a check on careless responding, although these do not otherwise contribute to scoring. The SIP was constructed by selecting, from each domain, the three items having the highest item-domain correlations. Blanchard, Morgenstern, Morgan, Lobouvie, and Bux (2003) created another version, choosing the 15 items with strongest item-total correlations. Two primary versions of SIP exist.
The lifetime version (“Has this EVER happened to you?”) is dichotomously scored. The recent consequences version uses a 4-point, frequency score scale with a 3-month window. The Inventory of Drug Use Consequences (InDUC; Miller et al., 1995) uses DrInC items reworded as “drinking or using drugs” instead of “drinking.” Allensworth-Davies, Cheng, Smith, Samet, and Saitz (2012) neatly summarized many SIP variants in their Figure 1 (p. 259).
Figure 1. Person score-item location map of Short Inventory of Problems (SIP) responses for Sample 1 (N = 10,312). Higher scale values represent items that were endorsed less often or persons with higher scaled scores; lower scale values represent items that were endorsed more often or persons with lower scores. Each “X” represents 50 respondents.
A number of studies have evaluated the structure of the SIP, either as part of the full DrInC/InDUC or specifically on the SIP or a variant (Allensworth-Davies et al., 2012; Alterman, Cacciola, Ivey, Habing, & Lynch, 2009; Anderson, Gogineni, Charuvastra, Longabaugh, & Stein, 2001; Blanchard et al., 2003; Bender, Griffin, Gallop, & Weiss, 2007; Feinn, Tennen, & Kranzler, 2003; Forcehimes, Tonigan, Miller, Kenna, & Baer, 2007; Gillespie, Holt, & Blackwell, 2007; Hagman et al., 2009; Kenna et al., 2005; Kiluk, Dreifuss, Weiss, Morgenstern, & Carroll, 2013; Marra, Field, Caetano, & von Sternberg, 2014; Tonigan & Miller, 2002). Table 1 summarizes outcomes for these studies of SIP structure.
Summary of Prior Factor Studies of SIP
To summarize, efforts to support anything more than a one-factor structure for the SIP have generally not fared well or, at best, not appreciably better than a unidimensional model. Miller et al. (1995), Forcehimes et al. (2007), and Alterman et al. (2009) explicitly attempted to confirm five-factor structures, with generally poor results. Kiluk et al. (2013) claimed better model-data fit for the five-factor over the one-factor model, although the differences in fit indices were generally slight, as was the case for Kenna et al. (2005). Beyond differences in wording (“drinking,” “drug use,” “drinking or drug use,” “bipolar disorder”) and sample characteristics, choices of how to analyze SIP data may have affected the results being reported, as well as the population sampled and the sample size. We have some methodological concerns with many of the studies that could account for some of the inconsistencies of results in studies of structure.
First, principal components analysis differs in important ways from common factor analysis. Gorsuch (1983) and Thompson (2004) point out that principal components extraction presumes a false reality (e.g., all observed variance is common; no unique or measurement error variance exists) and inflates observed variable-component loadings, especially with few variables. Second, for item-level data, tetrachoric (dichotomous) or polychoric (general form for ordered scales) correlations are preferred (Bock, Gibbons, & Muraki, 1988; Lorenzo-Seva & Ferrando, 2006; Muraki & Carlson, 1995). Third, most of the exploratory factor analysis (EFA) studies relied on dated criteria for determining the number of factors or components to extract (e.g., eigenvalue > 1.0, or scree plot). Simulation studies show better performance for parallel analysis or minimum average partial correlation methods to identify the number of latent variables (Kaufman & Dunlap, 2000; Lorenzo-Seva & Ferrando, 2006; O’Connor, 2000; Velicer, 1976). Fourth, although the SIP structure studies have generally used item-level data, researchers have occasionally used domain subscores (e.g., Anderson et al., 2001). Finally, for some of the EFAs and confirmatory factor analyses (CFAs), the sample sizes would be judged too small by many. For these reasons, and to help furnish more evidence toward understanding the latent structure of the SIP, we report here on the results of analyses of SIP responses from two massive samples of persons arrested for driving under the influence (DUI) and attending mandated alcohol safety training.
Method Participants
Sample 1 comprised 10,312 court-ordered DUI training participants having complete SIP data from July 2013 through October 2014. Most were male (78%), employed full-time (51%), or part-time/self-employed (20%). By ethnicity, 54% were Caucasian and 38% were African American. Hispanic/Latinos represented 2%. For marital status, most were never married (50%), followed by currently married (22%) or divorced (20%). Median age was 34 (M = 36.4, SD = 13.3). Blood alcohol concentration at arrest had a median value of 0.12% (n = 4,144; values above 0.499 were censored). Sample 2 was 21,670 adjudicated participants having complete SIP data from September 2008 through November 2011. Aside from year of court-ordered training, the two samples were quite similar in characteristics. As we used existing data, without personal identifiers, our university institutional review board (IRB) declared this research exempt from IRB review.
Instruments
The 15-item lifetime consequences version of the SIP was completed during the first class session of training for both samples. Both samples responded to the “drinking or drug use” version of the SIP, derived from the InDUC of Miller et al. (1995). For example, Item 11 is as follows: “A friendship or close relationship has been damaged by my drinking or drug use.”
Some ancillary indicators, to investigate criterion-related validity of SIP scores, were (a) Risky Driving Behaviors score (Sample 1 only), (b) Alcohol Consumption, and (c) Alcohol Problems score. The Risky Driving Behaviors score was the summed score of a nine-item set of poor driving behaviors with self-reported frequency on a 1–5 scale within the past 3 months. Two examples are (a) driven 20+ mph over the limit and (b) driven while using a cell phone. Estimated internal consistency reliability was .86. The Alcohol Consumption score was obtained by summing responses of frequency to three items: (a) number of days per week that one drinks, (b) number of drinks per occasion, and (c) frequency of having 5–6 or more drinks. Specific wording was slightly different; for Sample 2, we used wording from the Alcohol Use Disorders Identification Test (AUDIT, second edition; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). Estimated internal consistency reliabilities were .75 and .76 for Samples 1 and 2, respectively.
The Alcohol Problems Scale was obtained by summing responses of frequency to three items, also from the AUDIT: (a) “How often during the last year have you found that you were not able to stop drinking once you had started?” (b) “How often during the past year have you been unable to remember what happened the night before because you had been drinking?” and (c) “Has a relative, friend, doctor, or other healthcare worker been concerned about your drinking or suggested you cut down?” The response scale for the first two items was 0 = never, 1 = less than monthly, 2 = monthly, 3 = two to three times per week, and 4 = four or more times per week. The third item’s response scale was 0 = no; 1 = yes, but not in the last year; and 2 = yes, during the last year. Estimated internal consistency reliability values for the Alcohol Problems Scale were .71 (Sample 1) and .62 (Sample 2).
Data Analysis
Sample 1 responses were used to conduct an EFA using tetrachoric correlations among the 15 SIP items, calculated via the R library, psych (Revelle, 2016). Maximum likelihood extraction was used, via SPSS version 24. Upon determining a preferred structure, the data from Sample 2 responses were used to run a CFA, using LISREL 9.2 (Jöreskog & Sörbom, 2016). Regardless of the EFA result, the CFA was to compare at least three alternate models: a one-factor model, a five-factor model, and a five-factor model having a single, second-order factor.
We also conducted CFA analyses on both samples, using the five domain scores (physical, social, intrapersonal, impulse control, and interpersonal), to determine whether relationships among domain scores yielded comparable results to those derived from relationships among item scores. We used polychoric correlations among domain scores. With only five scores involved, we were only able to test fit to a one-factor model.
Criteria for good model-data fit in the CFA were (a) normed fit index and comparative fit index values of .90 or better, (b) root mean squared error of approximation (RMSEA) of .08 or less, and (c) standardized root mean square residual (SRMR) of .05 or less (Bollen, 1989; Hair, Black, Babin, & Anderson, 2010). We chose to ignore the overall chi-square test as a criterion, since it is so sensitive to sample size (Bollen, 1989).
We then conducted item response theory (IRT) estimates of SIP item parameters, via both the two-parameter logistic (2PL) and one-parameter logistic (1PL) models (Hambleton, Swaminathan, & Rogers, 1991). The ltm package for R (Version 1.0–0; Rizopoulos, 2006) was used to generate the two-parameter (item location or difficulty and item discrimination or slope) and one-parameter (item location or difficulty) estimates. We compared the overall fit of the 1PL versus 2PL results using the method of Kang and Cohen (2007). Finally, we determined relationships of SIP scores with selected drinking, risk, and behavioral indicators, mentioned in the Instruments section above.
ResultsSummary statistics for rates of item endorsement and for the five domain scores are given for each sample in Table 2. From these, we see that endorsement rates across samples were close, with a few exceptions. Endorsement of Item 5 (taken foolish risks) differed by more than 15% across samples, as did Item 6 (done impulsive things), while Item 14 (spent/lost too much money) differed by 9%. Other items showed lower differences, typically about 4% or less. The social and physical domain items tended to be endorsed less frequently than interpersonal and intrapersonal items. Overall, impulse control items tended to be endorsed most frequently.
Summary Statistics for Lifetime Consequence SIP Items, Domain, and Total Scores by Sample
EFA
Initial checks for the suitability of the SIP scores for factoring were promising. The Kaiser-Meyer-Olkin measure of sampling adequacy was high, .956. Bartlett’s test for an identity matrix was statistically significant (p < .001), indicating the presence of relationships. The minimum observed pairwise correlation was .396, with a median value of .653. Maximum likelihood extraction yielded results clearly favoring a one-factor structure. This conclusion was based on parallel analysis, although both the scree plot and eigenvalues affirm only one factor.
Item-factor loadings are given in Table 3. All were strong; the minimum loading was .556 (item 15) and median loading = .830. Regarding the adequacy of the structure to reproduce observed correlations, 85% of the 105 pairwise relationships yielded absolute residuals < |.05|. The SRMR was .048. We judge these results as supportive of a one-factor structure.
One-Factor Model Factor Loadings and Item Parameter Estimates
CFA: Item Scores
Analysis of the one-factor, five-factor, and second-order five-factor models yielded results favoring a modified one-factor model (see Table 4). With the exception of the RMSEA value, the original one-factor model as tested met all other criteria for acceptable model-data fit. Inspection of residuals suggested five item pairs might share some common variance in their errors: Items 1 and 4 (both were intrapersonal domain items on the DrInC), Items 5 and 6 (impulse control), Items 7 and 9 (physical), Items 8 and 14 (social), and Items 10 and 11 (Interpersonal). We thought it reasonable to estimate these common error covariances. Lest we be accused of back-pedaling our way to a five-factor model, we note that (a) other item pairs, drawing from the same domains, did not warrant such modifications, and (b) neither of the five-factor models yielded fit indices as good or was as parsimonious. Of all models, the modified one-factor model was the only one to meet all set criteria for model-data fit. Estimated loadings from this model are given in Table 2 and match very closely those from the EFA.
Fit Indices for Factor Models Compared Using Item-Level and Domain-Level Scores
We applied the modified one-factor model to the original data set (Sample 1), as no other data were available. The resulting fit indices were good and were superior—albeit not by a great margin—to those for the five-factor and five-factor with second-order factor models (see Table 4).
CFA: Domain Scores
Specification of a one-factor model yielded excellent indices of model-data fit (see Table 4) when the five item-domain scores were used as data points instead of item responses. For both samples, fit index values were excellent. The combination of item-level results with the domain-score level results affirms a one-factor model as suitable for the SIP.
Other Psychometric Information
Classical test theory results
Estimated internal consistency reliability (Cronbach’s alpha) of total SIP scores was very good for both samples. Each sample yielded an estimate of .91. That value is sufficiently high to warrant use of the SIP for making judgments about individuals, according to the guidelines of Cronbach (1990), and is consistent with those reported by others. Corrected item-total correlations yielded correlations ranging from .40 (Item 15) to .69 (Item 3) in Sample 1. Sample 2 yielded comparable values, from .39 (Item 15) to .68 (Item 12), which is unsurprising, given the similarity of factor structure information across samples reported earlier.
IRT results
Estimated item location and discrimination parameters for Sample 1 are reported in Table 3. Separate estimates were derived for Sample 2 and matched to a very high degree; R2 = .95 for location estimates across samples. SIP items differ both in location, with items such as Item 15 (had accident) and Item 9 (physical appearance harmed) being among the least likely to be endorsed, and items such as Item 6 (done impulsive things) and Item 5 (taken foolish risks) being the most likely to be endorsed. Discrimination, which relates to the certainty with which respondents can be distinguished at specific points along the scale continuum, varied considerably as well. Highly discriminating items included Item 3 (failed to do what’s expected), Item 11 (friendship/relationship damaged), and Item 12 (inhibited growth as person), whereas Item 15 was noteworthy for having much lower discrimination than other items. Items having low discrimination yield less information about a respondent’s location on the scale. We compared model fit for the simpler 1PL versus the 2PL, using the method of Kang and Cohen (2007), as implemented in the ltm package for R (Rizopoulos, 2006). For both samples, the likelihood ratio test was statistically significant (p < .001), favoring the 2PL model for use with the SIP items.
The match of item locations relative to participant scaled scores was such that the SIP scale appears to give reasonably good coverage of items to the range of scores observed on the SIP (see Figure 1). One obvious gap in the location of SIP items not matching well to the scaled scores obtained is for scaled scores below 0.3 (logits, or log units). Only four items have locations anywhere within the window of −1.3 through 0.3 (Items 4–6, 14). Better precision of respondent score estimates in that range could be possible with more items at the lower end of challenge (e.g., consequences that were more frequently endorsed). Similarly, there are no items having location higher than 1.11, corresponding to Item 15, which had the lowest discrimination. Thus, higher challenge consequences could also be helpful for the scale.
Other Validity Information
SIP scaled scores, derived from the IRT calibrations, were correlated with the Risky Driving index scores (Sample 1 only) as well as with the Alcohol Consumption and Alcohol Problems scores described earlier (see Table 5). In general, there was a noteworthy correlation of SIP scaled scores with Alcohol Problems scores (about .50 in both samples). Given that the nature of the DrInC/InDUC/SIP was to capture adverse consequences, this relationship is consistent with the purpose of the SIP. Correlation of SIP scores with Alcohol Consumption values was lower, .24–.25 in the samples. Risky Driving Behavior scores correlated lower yet with SIP scaled scores, r = .16. That the SIP scores correlate more strongly with alcohol use and problems than with a completely different domain of behavior (driving) makes intuitive sense. In our judgment, the pattern of relationships observed offers evidence for convergent (e.g., Alcohol Problems) and discriminant (e.g., Risky Driving) validity for the SIP scale.
Correlation of SIP Scaled Scores With Risky Driving, Alcohol Consumption, and Alcohol Problem Scores
DiscussionThe primary question prompting this research was that of the factor structure of the SIP. Both our literature review and our analyses persuade us that a one-factor model is the preferred structure. This is true whether working from item scores or the domain scores. Earlier studies favoring the more complex five-factor model (Kenna et al., 2005; Kiluk et al., 2013) showed only tiny differences in fit index values between competing models—not enough, in our opinion, to warrant the more complex model. Others attempting to confirm a five-factor model found little supporting evidence (Alterman et al., 2009; Forcehimes et al., 2007; Miller et al., 1995). Our study indicates that sample differences, drawing from the same population, have little impact on evidence for SIP structure, psychometrics, or scaling. Despite all the versions of the SIP (Allensworth-Davies et al., 2012), the behavior of the scale is remarkably consistent. That is not to say that the scale could not be improved, however.
Via IRT methods, we believe the scale could be improved for locating respondents with precision. Specifically, the SIP scale could be improved by adding items more likely to be endorsed by lower-scoring respondents, as well as items that would be endorsed by only higher-scoring respondents. Doing so would yield a SIP scale that could be helpful as a screener for “at-risk” clients, for predicting future outcomes, or to gauge response to treatment.
There are some limitations to acknowledge. First, we examined only the lifetime consequences version of the SIP. We believe this an issue more for IRT scaling of the items than for investigation of factor structure or criterion-related validity. Second, our investigation of structure yielded results for the competing models that were—like those of others—similar. What swayed our judgment were the considerations of parsimony and the congruence of item-level and subscore or domain-level support for the one-factor model. Third, our samples were, like the majority of the studies reviewed, predominately male. Only Gillespie et al. (2007), relying on college volunteers, had a sample that was predominately female. Although mean scores by sex have been reported (Anderson et al., 2001; Feinn et al., 2003; Miller et al., 1995), only the sample of Miller et al. (1995) was large enough to justify the exercise. Differential item functioning or structure by sex still remains to be addressed.
In sum, one can appreciate the versatility of the SIP over its 20-year plus existence. There is ample evidence for score reliability, usefulness with a variety of respondent populations, and construct and criterion-related validity. As a brief version of the DrInC/InDUC that is quickly administered, the SIP continues to serve ably the goals outlined by Miller et al. (1995).
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Submitted: October 11, 2016 Revised: December 6, 2016 Accepted: December 9, 2016
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Source: Psychology of Addictive Behaviors. Vol. 31. (1), Feb, 2017 pp. 110-116)
Accession Number: 2017-01384-001
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Record: 51- Risk pathways among traumatic stress, posttraumatic stress disorder symptoms, and alcohol and drug problems: A test of four hypotheses. Haller, Moira; Chassin, Laurie; Psychology of Addictive Behaviors, Vol 28(3), Sep, 2014 pp. 841-851. Publisher: American Psychological Association; [Journal Article] Abstract: The present study utilized longitudinal data from a community sample (n = 377; 166 trauma-exposed; 54% males; 73% non-Hispanic Caucasian; 22% Hispanic; 5% other ethnicity) to test whether pretrauma substance use problems increase risk for trauma exposure (high-risk hypothesis) or posttraumatic stress disorder (PTSD) symptoms (susceptibility hypothesis), whether PTSD symptoms increase risk for later alcohol/drug problems (self-medication hypothesis), and whether the association between PTSD symptoms and alcohol/drug problems is attributable to shared risk factors (shared vulnerability hypothesis). Logistic and negative binomial regressions were performed in a path analysis framework. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems, over and above the influences of pretrauma family risk factors, pretrauma substance use problems, trauma exposure, and demographic variables. Results partially supported the high-risk hypothesis, such that adolescent substance use problems increased risk for assaultive violence exposure but did not influence overall risk for trauma exposure. There was no support for the susceptibility hypothesis. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later substance use problems. Rather, PTSD symptoms mediated the effect of pretrauma family adversity on later alcohol and drug problems, thereby supporting the self-medication hypothesis. These findings make important contributions to better understanding the directions of influence among traumatic stress, PTSD symptoms, and substance use problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Risk Pathways Among Traumatic Stress, Posttraumatic Stress Disorder Symptoms, and Alcohol and Drug Problems: A Test of Four Hypotheses
By: Moira Haller
Department of Psychology, Arizona State University;
Laurie Chassin
Department of Psychology, Arizona State University
Acknowledgement: This study was supported by grants from the National Institute on Alcohol Abuse and Alcoholism (F31AA020698 and AA016213).
Exposure to traumatic events is surprisingly common. Approximately 61% of men and 51% of women experience at least one traumatic event during their lifetimes (Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). Traumatic events may lead to the development of not only posttraumatic stress disorder (PTSD), but also alcohol and drug problems. Rates of PTSD among adults with substance use disorders (SUDs) range from 14% to 60% (see Hien, Cohen, & Campbell, 2005). Studies of adolescents with SUDs indicate rates of PTSD ranging up to 20% (Deykin & Buka, 1997). Such high rates of comorbidity suggest that traumatic stress and trauma-related symptomatology may play a role in the etiology of SUDs or vice versa.
Several pathways may underlie the link between PTSD and SUDs. First, the “high-risk hypothesis” states that substance use or abuse may increase risk for exposure to a traumatic event by placing individuals in high-risk situations (e.g., Windle, 1994) or by impairing detection of danger cues in the environment (Davis, Stoner, Norris, George, & Masters, 2009). Second, the “susceptibility hypothesis” states that substance use or abuse may increase risk for developing PTSD among individuals who are exposed to a traumatic event (Chilcoat & Breslau, 1998a). For instance, substance use problems may interfere with the ability to effectively manage negative emotions resulting from the traumatic event, may increase anxiety and arousal levels attributable to substance withdrawal symptoms (Stewart, Pihl, Conrod, & Dongier, 1998), or may facilitate avoidance and lack of processing of trauma material (Kaysen et al., 2011). Third, the “self-medication hypothesis” states that individuals may use substances to cope with symptoms of posttraumatic stress (e.g., Chilcoat & Breslau, 1998b; Reed, Anthony, & Breslau, 2007). Finally, the “shared vulnerability hypothesis” (Stewart & Conrod, 2003) states that shared risk factors may account for both PTSD and alcohol/drug problems, such that PTSD and SUDs are not causally related once shared risk factors are taken into account.
Although these hypotheses are presented separately, they are not mutually exclusive and may be integrated into a larger, developmental model of PTSD-SUD comorbidity. For instance, preexisting adolescent substance use problems may not only increase individuals’ risk for trauma exposure (high-risk hypothesis), but may also make it more likely that they will turn to alcohol/drugs to cope with subsequent PTSD symptoms (self-medication hypothesis); this increased substance use may further exacerbate PTSD symptoms. The present study examines evidence for each of the four pathways that have been proposed to underlie PTSD-SUD comorbidity.
The High-Risk and Susceptibility HypothesesThe high-risk and susceptibility hypotheses both suggest that SUDs causally influence risk for PTSD. Retrospective and prospective studies examining these hypotheses have had mixed findings. Some studies show that substance use problems increase risk for trauma exposure but not PTSD (Bromet, Sonnega, & Kessler, 1998; Kilpatrick, Acierno, Resnick, Saunders, & Best, 1997), others show that substance use problems increase risk for PTSD but not trauma exposure (Acierno et al., 1999), and others show that substance use problems do not increase risk for either trauma exposure or PTSD after controlling for other risk factors (Chilcoat & Breslau, 1998a). Moreover, studies examining onset patterns of PTSD and SUDs in adults tend not to support the high-risk or susceptibility hypotheses, instead indicating that PTSD more often precedes than follows SUD onset (see Stewart & Conrod, 2003). This lack of compelling empirical support has led some researchers to conclude that it is unlikely that substance use problems causally influence risk for trauma exposure or PTSD, especially when considered among other risk factors (e.g., Chilcoat & Breslau, 1998a; Stewart & Conrod, 2003).
However, little prospective research has examined relations between adolescent substance use problems and risk for trauma exposure or PTSD. This limitation is important, given that risk for trauma exposure—particularly assaultive violence exposure—peaks between the ages of 16 and 20 (Breslau et al., 1998). It is currently unclear what role adolescent substance use problems may play in this heightened risk period for assaultive violence exposure. Because adolescent substance use problems may reflect a tendency to engage in particularly risky behavior, they may be more likely to result in trauma exposure or PTSD compared to adult substance use problems. Although retrospective data indicate that adolescents with SUDs are at two to five times the risk for experiencing a traumatic event (risk for exposure to an event involving violence is even higher), and at four to nine times the risk for developing PTSD (Deykin & Buka, 1997; Giaconia et al., 2000; Kilpatrick et al., 2000) than those without a SUD, prospective studies are needed to disentangle the direction of influence. In addition, studies that capture pretrauma risk stemming from subclinical levels of substance use problems are also needed, given that adolescent substance use problems, even if not meeting diagnostic threshold, may create meaningful risk for trauma exposure, PTSD, and posttrauma substance use problems. The current study addresses these limitations by testing the prospective effects of pretrauma adolescent substance use problems on risk for later trauma exposure (as well as risk for assaultive violence exposure, specifically) and PTSD symptoms.
The Self-Medication HypothesisIn contrast to the conflicting results from investigations of the high-risk and susceptibility hypotheses, there is a strong body of evidence supporting the self-medication hypothesis (Breslau, Davis, Peterson, & Schultz, 1997; Breslau, Davis, & Schultz, 2003; Chilcoat & Breslau, 1998b; Reed et al., 2007; Shipherd, Stafford, & Tanner, 2005; Ullman, Filipas, Townsend, & Starzynski, 2005; see also Hien et al., 2005, for review). Indeed, in a review of retrospective and prospective studies on PTSD-SUD comorbidity, Stewart and Conrod (2003, p. 37) summarized that “PTSD has been shown to develop before the SUD in the large majority of comorbid cases in retrospective studies, and PTSD has been shown to increase risk for SUDs in prospective studies.” Theoretically, individuals with PTSD may use alcohol and drugs to reduce irritability, concentration problems, hyperarousal, and so forth, raising risk for the development of SUDs.
The self-medication hypothesis implies a mediating role for PTSD symptoms in the relation between trauma exposure and substance use problems (Stewart, 1996). Support for the mediating role of PTSD comes from studies demonstrating that individuals who develop PTSD appear to be at higher risk for SUDs than do individuals who are exposed to a traumatic event but do not develop PTSD (Chilcoat & Breslau, 1998b; Breslau et al., 1998). However, these studies do not examine the extent to which PTSD symptom severity influences risk for future substance use problems. To address this limitation, the current study simultaneously estimated the unique effects of both PTSD symptoms and trauma exposure on future substance use problems in order to test the extent to which PTSD symptom severity influences risk for substance use problems, controlling for the effects of trauma exposure itself, as well as shared risk factors for PTSD and SUDs.
The Shared Vulnerability HypothesisThe high prevalence of PTSD-SUD comorbidity suggests that PTSD and SUDs may share a common etiological diathesis, including both environmental and genetic factors. There are a number of family-related risk factors that trauma exposure, PTSD, and SUDs may share in common. For instance, parental psychopathology has been shown to increase risk for offspring trauma exposure (Bromet et al., 1998; Koenen et al., 2002), PTSD (Brewin, Andrews, & Valentine, 2000; Bromet et al., 1998), and SUDs (Zhou, King, & Chassin, 2006). Importantly, parental psychopathology is also associated with other familial risk factors, such as higher levels of family conflict and higher levels of stress (Dube et al., 2003), which may further increase risk for trauma exposure and posttrauma psychopathology (Brewin et al., 2000; Deykin & Buka, 1997; Koenen, Moffitt, Poulton, Martin, & Caspi, 2007). Research suggests that individuals who grow up in adverse family environments may be sensitized to the effects of future stressors (Koenen et al., 2007), thus placing them at risk for posttrauma maladjustment. Adolescents from adverse family environments may also be less likely to have the resources and supports necessary for effectively coping with a traumatic event. Therefore, the familial backdrop against which trauma occurs is likely to be a key determinant of posttrauma functioning. Yet, the extent to which pretrauma adversity in the family environment is a shared risk factor for PTSD and later substance use problems is currently unclear because so few studies contain pretrauma measures of family risk factors. The present study thus examines how pretrauma family adversity may influence risk for PTSD symptoms and/or substance use problems in order to better understand the role of family risk factors in PTSD-SUD comorbidity.
In addition to directly increasing risk for PTSD and substance use problems, it is also possible that adolescent family adversity may indirectly influence risk for posttrauma substance use problems by increasing risk for PTSD symptoms (i.e., PTSD symptoms may mediate the influence of preexisting family adversity on later alcohol and drug problems). Although finding that PTSD symptoms mediate the influence of preexisting family adversity on later alcohol and drug problems would support the self-medication hypothesis rather than the shared vulnerability hypothesis, such findings would nonetheless highlight preexisting family adversity as an important developmental antecedent in the PTSD-SUD link.
To test whether family adversity during adolescence accounts for the link between PTSD symptoms and posttrauma substance use problems (i.e., the shared vulnerability hypothesis), it is important to control for preexisting substance use problems that may have already been present and that co-occurred with adolescent family adversity. Indeed, adolescents who grow up in adverse (i.e., high-conflict, high-stress) family environments are also more likely to misuse alcohol and drugs (e.g., Repetti, Taylor, & Seeman, 2002; Zhou et al., 2006), regardless of trauma exposure or PTSD. Thus, to disentangle the directions of influence among traumatic stress, PTSD, and problematic substance use, both preexisting adolescent substance use problems and the confounding influence of the larger constellation of family adversity must be accounted for.
Trauma exposure itself may be conceptualized as a shared environmental risk factor for PTSD and SUD (Yehuda, McFarlane, & Shalev, 1998). Although traumatic events are most often associated with PTSD, they may also precipitate SUDs independent of their effects on PTSD, such that PTSD-SUD comorbidity reflects the co-occurrence of distinct diatheses. For instance, individuals who are predisposed to biological hyper-responsiveness may experience further sensitizations in their stress response systems after trauma exposure and may thus develop PTSD (Yehuda et al., 1998), whereas individuals with other predispositions may experience a range of other stress responses that lead to other disorders, such as SUDs. If this hypothesis were true, traumatic stress would be expected to directly predict substance use problems, separate from its influence on PTSD. Alternatively, a direct effect of traumatic stress on problematic alcohol/drug use (separate from the effects of PTSD) would not be expected if other common risk factors account for the link between PTSD symptoms and alcohol/drug problems. Previous tests of these hypotheses show that trauma-exposed individuals who do not develop PTSD are not at increased risk for subsequent onset of SUDs, but those who develop PTSD are (Breslau et al., 2003; Chilcoat & Breslau, 1998b; Reed et al., 2007; see Fetzner, McMillan, Sareen, & Asmundson, 2011 for an exception). However, the “trauma exposure as a shared risk factor” hypothesis has not yet been tested using analytic strategies other than comparing risk for onset of clinical SUDs among individuals with PTSD to individuals with trauma exposure who do not have PTSD. Therefore, the current study tested the effect of trauma exposure on later substance use problems, while controlling for a count of the number of PTSD symptoms that participants endorsed.
The Present StudyThe purpose of the present study was to better understand the risk pathways that link trauma exposure, PTSD, and alcohol and drug problems. Alcohol and drug problems were examined as separate outcomes based on previous studies that have found that traumatic stress and PTSD symptoms may have differential relations with alcohol versus drugs (e.g., Breslau et al., 2003; Haller & Chassin, 2012; Shipherd et al., 2005). Specifically, this study tested the following hypotheses (see Figure 1), which are not mutually exclusive:
High-risk hypothesis. Do adolescent substance use problems increase risk for trauma exposure or assaultive violence exposure over and above the influence of preexisting family risk factors and demographic predictors?
Susceptibility hypothesis. Do adolescent substance use problems increase risk for PTSD symptoms among individuals exposed to trauma over and above the influence of preexisting family risk factors and demographic predictors?
Self-medication hypothesis. Do PTSD symptoms increase risk for future alcohol and/or drug problems over and above the influences of trauma exposure itself, pretrauma substance use problems, demographic predictors, and preexisting family risk factors that are common to both PTSD and alcohol/drug problems?
Shared vulnerability hypothesis. Do trauma exposure and/or adversity in the family environment account for the link between and substance use problems, such that PTSD symptoms and substance use problems are not related when these potential shared risk factors are accounted for? By testing this hypothesis, this study also addresses a series of related questions: To what extent does adversity in the family environment increase risk for both PTSD and alcohol/drug problems? To what extent is the influence of preexisting family risk factors on adult alcohol and drug problems mediated by PTSD symptoms? Is trauma exposure a shared risk factor for both PTSD and substance use problems, such that trauma exposure increases risk for future alcohol or drug problems, independent of PTSD symptoms?
Figure 1. Simplified depiction of model estimation and paths relevant to hypothesis testing. a Note that Wave 4 measures reflect trauma exposure and PTSD symptoms that occurred between Waves 1 and 4 (average age at exposure was 17.3).
These hypotheses were tested using data from a longitudinal, community-based study of familial alcoholism, which is important given that most research on the overlap between PTSD and substance use problems consists of cross-sectional, retrospective, and clinic-based studies without preexisting measures of substance use and associated family risk factors. By using a high-risk sample with elevated prevalence of risk factors, trauma, and substance use problems, the present study was particularly well-suited for examining the hypothesized pathways.
Method Participants
Participants (n = 377) were from a larger longitudinal study of familial alcoholism (Chassin, Barrera, Bech, & Kossak-Fuller, 1992). The original study had three annual waves of data collection and three additional follow-ups separated by five years. The present study used data from Waves 1 (1988), 4 (≈1995), and 5 (≈2000). Both parents and adolescents were interviewed at each time point. At Wave 1, there were 454 “target” adolescents between the ages of 11 and 15 and their parents; 246 adolescents had at least one biological parent with an alcohol disorder who was also a custodial parent, and the remaining 208 adolescents were demographically matched controls without any biological or custodial parents with an alcohol disorder. Sample retention ranged from 90% to 99% across waves.
Participants reported their history of trauma exposure and PTSD at Wave 4 (seven to 10 years after the initial assessment). Forty-seven participants who were not interviewed at Wave 4 were excluded from the present study. Thirty participants who reported trauma exposure before Wave 1 were also excluded so that Wave 1 measures preceded trauma exposure for all participants. Thus, our final sample consisted of 377 participants (54% male; 52% children of parents with an alcohol disorder; 73% non-Hispanic Caucasian; 22% Hispanic, 5% other ethnicity; mean age = 13.2 at Wave 1, 20.4 at Wave 4, and 25.6 at Wave 5).
Analyses examining differences between the 377 participants included in this study and the 77 excluded participants showed that excluded participants were more likely to be children of alcoholic parents, χ2(1) = 6.65, p = .01. However, included and excluded participants did not differ in gender, ethnicity, parental psychopathology other than alcoholism, family conflict, family life stress, adolescent substance use problems, or adult alcohol or adult drug problems.
Recruitment and Procedure
Families with parental alcoholism were recruited using court DUI records, questionnaires from newly enrolled members of a large HMO, and community telephone surveys. Matched nonalcoholic families (matched on child’s age, family composition, ethnicity, and SES) living in the same neighborhoods as the families with parental alcoholism were recruited via telephone surveys. Potential participants who were successfully contacted did not differ from those who were not contacted on available alcoholism indicators (e.g., blood alcohol level at time of arrest, number of prior alcohol-related arrests). See Chassin et al. (1992) for details. After parents provided informed consent and adolescents provided assent, interviews were conducted in person with computer-assisted interviews (parents and adolescents were interviewed in separate rooms), or via telephone (after verifying private conditions) for out-of-state families. All protocols were approved by the Arizona State University Institutional Review Board.
Measures
Adolescent substance use problems
At Wave 1, adolescents reported on 14 (Cronbach’s alpha = .86) lifetime problems (e.g., receipt of complaints from family or friends) that they may have experienced as a result of alcohol or drug use. Items were adapted from Sher’s (1987) questionnaire assessing substance use problems among college students. Given the low frequency of adolescents with high counts of substance use problems (only 42% of adolescents reported having ever used alcohol or drugs), analyses used a variable that was coded 0 if the adolescent reported no lifetime substance use problems (n = 317; 84.1%), 1 if the adolescent reported one lifetime substance use problem (n = 23; 6.1%), and 2 if the adolescent reported two or more lifetime substance use problems (n = 37; 9.8%).
Adolescent’s family adversity
At Wave 1, family adversity was measured via a cluster of related family variables (family conflict, family stress, parental alcoholism, and other parent psychopathology) associated with trauma exposure, PTSD, and SUDs. These variables are likely to reflect a combination of both genetic and environmental risk. Correlations among the four family factors were all significant (see Table 1; ps < .001). To avoid multicollinearity problems, analyses used a composite “family adversity” variable derived using factor scores (M = 0.00; SD = 0.84, range: −1.91 to 2.36) from a one-factor confirmatory factor analysis. The family adversity factor score was significantly associated with trauma exposure, PTSD symptoms, adult alcohol problems, and adult drug problems (see Table 1). Thus, this variable appeared to appropriately capture shared risk for these outcomes.
Zero-Order Correlations
Both family conflict and familial life stress were measured via adolescent, mother, and father reports. Family conflict was a composite of all reports, each measured via four items (e.g., “we fought a lot in our family”) from Bloom’s (1985) Family Process Scale with responses ranging from Strongly Disagree to Strongly Agree (M = 2.74; SD = 0.60; range: 1.33–4.38; Cronbach’s alphas were .62, .63, and .63 for adolescent, mother, and father report). Familial life stress was a count of 15 independent events (e.g., serious money troubles; M = 3.20; SD = 2.36; range: 0–11) from the General Stressful Life Events Schedule for Children (Sandler, Ramirez, & Reynolds, 1986) and the Children of Alcoholics Stressful Life Events Schedule (Roosa, Sandler, Gehring, & Beals, 1988). Parent alcoholism (Diagnostic and Statistical Manual of Mental Disorders, third edition [DSM–III] abuse or dependence) was measured via parents’ self-reports on the Computerized Diagnostic Interview Schedule (CDIS-III; Robins, Helzer, Croughan, & Ratcliff, 1981), or via spousal report for noninterviewed parents using Family History-Research Diagnostic Criteria, Version 3 (Endicott, Anderson, & Spitzer, 1975; 51.5% of adolescents had at least one biological parent with an alcohol disorder who was also a custodial parent). Other parent psychopathology (DSM–III affective, anxiety, or antisocial personality disorder) was measured via parents’ self-reports on the CDIS-III (39.5% of adolescents had a parent with one of these disorders).
Late adolescent/early adult trauma exposure and PTSD symptoms
At Wave 4, the computerized Diagnostic Interview Schedule (CDIS-III-R; Robins, Helzer, Cottler, & Golding, 1989) was used to assess participants’ lifetime exposure to trauma and PTSD symptoms using DSM–III–R criteria. Participants reported on up to three traumatic events and 17 PTSD symptoms (118 [71%] participants reported one event; 33 [9%] participants reported two events, and 15[4%] participants reported three events). On average, fewer than 3 years (M = 2.65, SD = 1.70) elapsed between the time of the traumatic event and the assessment of PTSD. Among trauma-exposed participants (n = 166; 44%; mean age at exposure = 17.3 years.), 72 (43%) experienced at least one event involving assaultive violence (rape, physical assault or being threatened with a weapon), whereas 94 (57%) experienced other types of events (seeing someone hurt or killed, natural disaster, narrow escape from death/injury, sudden injury/accident, sudden death/injury of someone close, experiencing shock from other’s experience, or other event). See Haller and Chassin (2012) for rates of each type of event. Analyses used a dichotomous measure of trauma exposure, and a count variable that indicated the total number of PTSD symptoms for whichever event produced the highest number of symptoms (M = 5.41 symptoms, SD = 4.11). Thirty-one participants (19% of trauma-exposed) met criteria for PTSD.
Adult alcohol and drug problems
At Wave 5, participants reported on 17 problems (e.g., failed attempts to cut down) as a result of alcohol and drug use (note that three substance use problems were assessed at Wave 5 that were not assessed during adolescence). Follow-up questions assessed the recency of each problem separately for alcohol and drugs. Analyses used count variables indicating the total number of adult alcohol problems and drug problems (separately) experienced in the past two years at Wave 5. The two-year timeframe allowed for prospective prediction of adult alcohol and drug problems from PTSD symptoms. Cronbach’s alpha was .85 for alcohol and .91 for drugs. Participants who drank at Wave 5 (82%) reported drinking on average more than “5 times in the past year” but less than “1–3 times a month.” Twenty-nine percent of participants reported using drugs at Wave 5, with marijuana being the most commonly used drug. At Wave 5, 44% of interviewed participants experienced at least one alcohol problem in the past two years (M = 1.54, SD = 2.58, range: 0–13), and 19% experienced at least one drug problem in the past two years (M = 0.83, SD = 2.33, range: 0–13).
Data Analytic Strategy
All analyses were conducted in MPlus version 6.11 (Muthén & Muthén, 1998–2011). Models were estimated using the maximum likelihood estimator with robust standard errors (MLR) to ensure robustness against heteroscedasticity, non-normality, and model misspecification. Full information maximum likelihood estimation was used to account for missing data for 29 participants who were not interviewed at Wave 5.
Figure 1 presents a simplified depiction of model estimation and those paths that are relevant to hypothesis testing. Adult alcohol problems and adult drug problems were examined in separate models. Each model included three endogenous variables: trauma exposure (binary), PTSD symptoms (count variable), and adult alcohol/drug problems (count variables). Logistic regression was used to predict trauma exposure, and negative binomial regression was used to predict PTSD symptoms and adult alcohol/drug problems. Incidence rate ratios (IRRs) are presented for count outcomes (e.g., an IRR of 1.10 means that for every one unit increase in the predictor, there is a 10% increase in the dependent variable). Because PTSD symptoms are conditional upon trauma exposure, data were specified as missing on the count measure of PTSD symptoms for participants who were not exposed to a traumatic event (i.e., those coded 0 on the binary trauma exposure variable). Paths were specified from adolescent family adversity, adolescent substance use problems, gender (coded 0 for males and 1 for females), and ethnicity (coded 0 for non-Hispanic Caucasians and 1 for other ethnicities) to each endogenous variable. Paths were also specified from trauma exposure and PTSD symptoms to adult alcohol/drug problems. The residual covariance between trauma exposure and PTSD symptoms was estimated to allow for the fact that they may share predictors other than those specified in the model. In addition to the primary models, a separate logistic regression was conducted to test whether adolescent substance use problems significantly increase risk for assaultive violence exposure (i.e., high-risk hypothesis), over and above family adversity, gender, and ethnicity.
Before conducting the main analyses, preliminary analyses tested for significant covariates (gender, ethnicity, parent education, age, age at trauma exposure, and time since trauma exposure), covariate by predictor interactions, and predictor by predictor interactions. Preliminary analyses indicated a significant effect of time since trauma exposure on risk for alcohol problems, an interaction between family adversity and gender when predicting risk for drug problems, an interaction between gender and ethnicity when predicting trauma exposure, and interactions between PTSD symptoms and ethnicity when predicting alcohol and drug problems. These effects were retained in the final models (as shown in Table 2). All other covariate effects and interactions were nonsignificant and were not further considered. However, gender and ethnicity were retained as covariates in all models, given numerous gender and ethnic differences in both the trauma/PTSD and SUDs literatures.
Results From Primary Analyses
ResultsCorrelations among study variables are presented in Table 1. As expected, males were more likely to be exposed to a traumatic event than were females (r = −.15, p = .003), but trauma-exposed females exhibited higher levels of PTSD symptoms (r = .35, p < .001) than trauma-exposed males. Males exhibited higher levels of adult alcohol (r = −.21, p < .001) and drug (r = −.12, p = .029) problems than did females. Trauma-exposed participants were at significantly higher risk for adult alcohol problems (r = .12, p = .024) and at marginally higher risk for adult drug problems (r = .09, p = .096), than were participants who were not exposed to a traumatic event. Among trauma-exposed participants, PTSD symptoms were not significantly associated with adult alcohol problems (r = .11, p = .198) and were only marginally associated with adult drug problems (r = .14, p = .089). However, partial correlations revealed that after controlling for gender, there was a significant association between PTSD symptoms and both alcohol (pr = .22, p = .006), and drug (pr = .21, p = .011) problems. Gender was specified as a covariate in all analyses.
Table 2 presents results from the primary analyses. Results showed that the unique effect of adolescent substance use problems on risk for trauma exposure was nonsignificant (high-risk hypothesis; B = 0.21, p = .33, OR = 1.23), over and above pretrauma family adversity, gender, and ethnicity. However, a separate logistic regression indicated that there was a small unique effect of adolescent substance use problems on risk for assaultive violence exposure (B = 0.38, p = .051, OR = 1.46; results not shown in table).
In terms of the susceptibility hypothesis, adolescent substance use problems did not significantly increase susceptibility for developing PTSD symptoms (B = 0.07, p = .40, IRR = 1.07) among participants exposed to a traumatic event over and above the influence of correlated adversity in the family environment. Follow-up analyses showed that if family adversity were excluded from the model, adolescent substance use problems would have had significant effects on both trauma exposure (B = 0.41, p = .045, OR = 1.50) and PTSD symptoms (B = 0.15, p = .04, IRR = 1.16).
In terms of the self-medication hypothesis, results showed that PTSD symptoms had a significant unique effect on future adult alcohol (B = 0.09, p = .003, IRR = 1.10) and drug problems (B = 0.09, p = .042, IRR = 1.10), over and above the effects of trauma exposure, pretrauma substance use problems, family adversity, and covariates. However, preliminary analyses indicated a significant interaction between ethnicity and PTSD symptoms in the model predicting alcohol problems (B = −0.10, p = .02, IRR = 0.90), and a marginally significant interaction in the model predicting drug problems (B = −0.15, p = .06, IRR = 0.86). Probing these interactions indicated that the influence of PTSD symptoms on both alcohol drug problems was significant for non-Hispanic Caucasians but not for minority ethnicities (see note “c” in Table 2). To allow comparisons specifically between Hispanics and non-Hispanic Caucasians, analyses were repeated while excluding the 18 participants of other ethnicities. Results were consistent; the effect of PTSD symptoms on alcohol and drug problems was significant for non-Hispanic Caucasians but not for Hispanics.
Finally, we examined evidence for the shared vulnerability hypothesis. Because PTSD symptoms were significantly related to both alcohol and drug problems while accounting for family adversity and trauma exposure, this hypothesis was not supported. We subsequently examined the paths from family adversity to trauma exposure, PTSD symptoms, alcohol problems, and drug problems to test the extent to which family adversity increased risk for these outcomes. Although family adversity had a significant effect on both trauma exposure and PTSD symptoms, its direct effect on alcohol problems (the “c” path in a mediational model) was nonsignificant (B = 0.01, p = .92, IRR = 1.01). Mediational analyses showed that PTSD symptoms significantly and fully mediated the effect of pretrauma family adversity on alcohol problems (95% CI = [0.010, 0.038]), while controlling for gender, ethnicity, and pretrauma substance use problems. As for drug problems, the direct effect of family adversity on risk for drug problems was significant for females (B = 1.12, p < .001, IRR = 3.06) but not for males (B = −0.01, p = .95, IRR = 0.99). Thus, although PTSD symptoms significantly mediated the influence of family adversity on drug problems (95% CI = [0.001, 0.057]), there appeared to be full mediation for males but only partial mediation for females.
With respect to the theory that trauma exposure itself may be conceptualized as a shared risk factor, trauma exposure did not have a unique effect on either alcohol (B = 0.17, p = .38, IRR = 1.19) or drug problems (B = 0.20, p = .55, IRR = 1.22).
DiscussionThe present study tested a series of hypotheses to help explain the risk pathways that link traumatic stress, PTSD symptomatology, and alcohol and drug problems. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems participants, over and above the influences of pretrauma family adversity, pretrauma substance use problems, trauma exposure, and demographic variables. As for the reverse direction (the influence of substance use problems on risk for trauma exposure or PTSD), the high-risk hypothesis was partially supported but only with respect to trauma exposure that involved assaultive violence. That is, pretrauma adolescent substance use problems did not significantly influence overall risk for trauma exposure over and above the influence of pretrauma family adversity, but did have a marginally significant unique effect on risk for assaultive violence exposure. Moreover, pretrauma binge drinking was significantly associated with increased risk of assaultive violence exposure. There was no support for the susceptibility hypothesis, as pretrauma adolescent substance use problems did not significantly influence risk for PTSD symptoms over and above the influence of pretrauma family adversity. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later alcohol and drug problems. Findings are explored in greater detail below.
High-Risk and Susceptibility Hypotheses
The present study is among the first to test whether adolescent substance use problems prospectively predict increased risk for trauma exposure or PTSD symptoms. Importantly, the nonsignificant effect of adolescent substance use problems on risk for both trauma exposure and PTSD would have been significant if pretrauma family adversity were excluded from the model. This finding suggests that it is the high-risk family context within which problematic adolescent substance use occurs that may increase risk for future trauma exposure and PTSD symptoms, rather than adolescent substance use problems themselves. Trauma-exposed adolescents from adverse family environments may lack the safe context, resources, and social support needed to effectively cope with a traumatic event. These results highlight the importance of considering family adversity as an important contextual risk factor in models of PTSD-SUD risk to avoid making false conclusions about the about the extent to which associated individual behaviors lead to trauma exposure and posttrauma maladjustment. Although previous retrospective data indicate that adolescents with SUDs are at greatly elevated risk for both trauma exposure and PTSD compared to adolescents without SUDs (Deykin & Buka, 1997; Giaconia et al., 2000; Kilpatrick et al., 2000), such findings likely reflect the large body of risk factors associated with adolescent SUDs.
In contrast to the nonsignificant effect of adolescent substance use problems on risk for overall trauma exposure, adolescent substance use problems did have a marginally significant effect on risk for assaultive violence exposure (events involving rape, physical assault or being threatened with a weapon), even after accounting for the significant influence of co-occurring family adversity. Further, post hoc analysis showed that adolescent binge drinking significantly increased risk for exposure to assaultive violence, over and above the effect of family adversity. These findings are important given that risk for assaultive violence exposure, which carries an especially high risk for developing PTSD compared with other types of traumatic events (Kessler et al., 1995), is especially high during late adolescence/early adulthood (Breslau et al., 1998). Adolescent substance misuse, such as binge drinking, may be one factor driving this risk. Importantly, this finding suggests that programs to prevent adolescent substance abuse may have the added benefit of reducing assaultive violence exposure, thus also reducing risk for PTSD.
There are several reasons why substance use problems and binge drinking may place adolescents at risk for assaultive violence exposure. Risky substance use, such as binge drinking, may impair judgment and one’s ability to discern danger cues in the environment. Moreover, compared with adults, adolescents may be more likely to use substances outside of the home to avoid adult supervision, which may place them in dangerous situations. Adolescents may also engage in unsafe activities while under the influence or during their efforts to obtain alcohol and drugs. In addition, adolescent substance abusers are especially likely to associate with deviant peers who engage in delinquent behaviors (Fergusson, Swain-Campbell, & Horwood, 2002), which may thereby increase their risk for assaultive violence. Finally, given that the average age at which adolescent substance use problems were measured was 13.2 years old, it is possible that those individuals who experience substance use problems so early in life constitute a particularly high-risk group that is likely to engage in multiple risk behaviors (e.g., stealing, fighting, early initiation of sex), any of which may increase their risk for being exposed to violence. Indeed, a recent study found that adolescent boys who engaged in high-risk behaviors (i.e., alcohol use, drug use, and delinquent behavior) were at increased risk for exposure to physical assault and/or witnessed violence later in adolescence (Begle et al., 2011).
The Self-Medication and Shared Vulnerability Hypotheses
This study adds to a growing literature in support of the self-medication hypothesis, such that individuals may use alcohol and drugs to cope with PTSD symptoms and are thus at increased risk for substance use problems. Indeed, for each additional PTSD symptom, risk for alcohol and drug problems increased approximately 10%. Findings extend previous research on the self-medication hypothesis in several ways. First, this study accounted for the influence of preexisting, subclinical levels of substance use problems. Previous research has typically examined patterns of onset among PTSD and SUDs, which ignores the role that subclinical levels of substance use problems may play in risk for both trauma exposure and posttrauma maladjustment. Moreover, by including both pre- and posttrauma measures of substance use problems, the present study allowed for inferences regarding the direction of effect.
Second, the present study differentiated between the effects of trauma exposure and PTSD symptoms on future substance use problems. Few studies have recognized that trauma exposure may be a shared risk factor for both PTSD and SUDs. The fact that trauma exposure failed to significantly influence risk for alcohol or drug problems while controlling for subclinical levels of PTSD provides strong evidence that the effects of traumatic stress on substance use problems are mediated by PTSD symptoms. Even though the majority of trauma-exposed individuals do not develop clinically significant PTSD (Kessler et al., 1995), this study suggests that trauma exposure may nonetheless have meaningful effects on one’s risk for future substance use problems to the extent that there are resultant posttraumatic symptoms.
Third, the present study advances previous research on the self-medication hypothesis by controlling for the confounding influence of preexisting adversity in the family environment. Findings highlight adolescent family adversity as an important risk factor for trauma exposure, PTSD, and adult substance use problems, alike. However, there was no evidence that family adversity accounted for the association between PTSD and either alcohol or drug problems. The influence of family adversity on alcohol problems was fully mediated by PTSD symptoms; the influence of family adversity on drug problems was fully mediated by PTSD symptoms for males but only partially mediated by PTSD symptoms for females. Although the effects of family adversity on alcohol and drug problems were generally indirect rather than direct, findings nonetheless suggest that preexisting family adversity plays an important role in the PTSD-SUD link. Indeed, results provided evidence for a causal chain, whereby family adversity increased risk for trauma exposure and PTSD symptoms, which in turn increased risk for later adult alcohol and drug problems. Thus, it appears that family adversity operates as an important contextual risk factor such that trauma-exposed individuals who grow up in adverse family environments are more likely to develop PTSD symptoms and later substance use problems.
Fourth, although previous research has made it clear that substance use problems are prevalent in the aftermath of trauma (Stewart & Conrod, 2003), the present study extends this knowledge by demonstrating that the effects of PTSD on substance use problems persist well into the future. This finding is consistent with a study by Swendsen and colleagues (2010), which showed that PTSD diagnosis prospectively predicted onset of alcohol and drug dependence 10 years later. Finally, the present study provided tentative evidence that the self-medication hypothesis may vary across ethnicity, such that PTSD symptoms increase risk for substance use problems for non-Hispanic Caucasians but not Hispanics. However, given the small sample size, replication of this finding is needed before definitive conclusions can be made.
This study’s finding that PTSD symptoms directly increased risk for both alcohol and drug problems differs from a previous study using this same sample, which examined externalizing and internalizing symptoms as mediators of the influence of PTSD symptoms on alcohol and drug problems (Haller & Chassin, 2012). This previous study found that PTSD symptoms directly influenced risk for adult drug problems, but PTSD symptoms only influenced risk for adult alcohol problems to the extent that PTSD symptoms increased early adult externalizing symptoms. Several methodological differences may help explain the difference in findings between the present study and the Haller and Chassin (2012) study. First, the present study included both individuals who were and were not exposed to trauma in its analysis, whereas the previous study included only trauma-exposed participants. Second, the present study accounted for family adversity, ethnicity, and trauma exposure, whereas the previous study did not. Third, this study used a count of alcohol/drug problems as its outcome variable, whereas the previous study used a composite of frequency of use and problems within a shorter timeframe (only one year). Thus, the outcome variable in the current study reflects a more severe measure of alcohol problems. It is possible that PTSD symptoms are more strongly related to problematic alcohol use than to alcohol use itself.
Despite these methodological differences, findings from the Haller and Chassin (2012) study have important implications for the present study. Haller and Chassin distinguished between a PTSD-specific self-medication mechanism, and a more generalized negative affect self-medication mechanism (e.g., Khantzian, 1985), such that individuals may use substances to reduce negative affect and other internalizing symptoms. Importantly, Haller and Chassin found that PTSD-related increases in internalizing symptoms did not significantly increase risk for either alcohol or drug problems. Thus, it appears to be PTSD symptoms, specifically, that increase risk for substance use problems, rather than broader internalizing symptomatology (e.g., sad mood, low energy, worthlessness) that is often experienced during the aftermath of trauma.
Limitations and Conclusions
Several study limitations should be noted. First, many factors outside the scope of this study (e.g., peritraumatic factors, genetic influences) may influence risk for trauma exposure and/or posttrauma adjustment. Although this study failed to support the shared vulnerability hypothesis with respect to trauma exposure and family adversity, many other shared risk factors may contribute to the association between PTSD and SUDs. Similarly, despite the lack of support for the susceptibility hypothesis, future studies of moderators may find that preexisting substance use problems may indeed increase risk for PTSD for certain individuals or under certain conditions. Second, it was not possible to examine reciprocal relations between PTSD symptoms and substance use problems over time because PTSD symptoms were assessed at only one time point. Third, findings may not generalize to those with very early trauma exposure, given that we excluded participants who experienced trauma before Wave 1. Fourth, adolescent substance use problems were measured at a very young age and, on average, four years before trauma exposure. Measures closer in time to the traumatic event will be better suited to testing the true extent to which preexisting substance use problems are a causal risk factor for trauma exposure and/or PTSD; however, the unpredictable timing of trauma exposure makes it nearly impossible to obtain such a measure. Finally, trauma exposure and PTSD symptoms were assessed using DSM–III–R criteria rather than the current DSM-5 criteria.
In summary, this study is among the few longitudinal, community-based studies to test the directions of influence among trauma exposure, PTSD, and alcohol and drug problems. Results demonstrated that PTSD symptoms may have long-lasting effects on substance use problems, thereby highlighting PTSD symptomatology as an important etiological factor in the development of SUDs. Findings also indicated that family environments characterized by high levels of conflict, stress, and psychopathology may influence risk for posttrauma substance use problems by increasing the likelihood of developing PTSD symptoms after a traumatic event. Finally, this study also provided support for adolescent substance use problems and binge drinking as risk factors for assaultive violence exposure, which conveys an especially high risk for PTSD compared with other traumatic events (Kessler et al., 1995) and may thus increase the likelihood of posttrauma substance use problems. Findings are thus consistent with the notion that multiple, nonmutually exclusive pathways may underlie the link between PTSD and SUDs.
These findings have implications for preventing substance use problems among individuals who present for treatment for PTSD. Clinicians should routinely assess clients’ risk for using alcohol or drugs to self-medicate PTSD symptoms, discuss long-term dangers associated with self-medication, and provide other means of coping. Findings also highlight the need to screen for and treat PTSD symptomatology among individuals who present with substance use problems. Research indicates a low detection rate of PTSD within addiction treatment centers because individuals with substance use problems often to do not report traumatic experiences and PTSD symptoms unless specifically asked (Kimerling, Trafton, & Nguyen, 2006). Individuals with concurrent PTSD symptoms and SUDs are especially hard to treat, and do not optimally benefit from standard SUD interventions (Norman, Tate, Anderson, & Brown, 2007). Findings from the present study suggest that in addition to addressing the functional associations between PTSD symptoms and problematic substance use, resolving distress related to adversity in the family environment may also be a potentially important treatment target. Understanding the development and treatment of co-occurring PTSD symptoms and substance use problems remains an important area for research.
Footnotes 1 Trauma exposure is by definition a risk factor for PTSD symptoms, but this study clarifies whether trauma exposure may also directly increase risk for future substance use problems.
2 Analyses modeled the effects of substance use problems, rather than substance use itself, because problems were expected to be more prognostic of future risk for trauma exposure, PTSD, and adult substance use problems. That is, adolescents who were using substances to such an extent that they were already experiencing abuse or dependence symptoms were theorized to exhibit a high-risk substance use style that may place them at risk for trauma, PTSD, and/or substance use problems. Moreover, modeling the effects of substance use problems allowed us to be longitudinally consistent when predicting adult substance use problems.
3 This study examined risk for substance use problems at Wave 5 rather than Wave 4 (when trauma/PTSD was assessed) to provide a prospective test of PTSD symptoms on future substance use problems. Analyses controlled for Wave 1 (pretrauma) substance use problems when examining risk for Wave 5 substance use problems.
4 The assessment of drug-related problems referred to drugs in general rather than a specific class of drugs (how recently have you used a drug enough so that that you felt like you needed or depended on it?). Before beginning these questions, the interviewer stated When we ask you about drug use we do NOT mean medicines that were given to you by your doctor. We want to know about your use of drugs that were not prescribed by your doctor.
5 Analyses were repeated after deleting the 29 participants who were missing data at Wave 5 (n = 348). Results were unchanged. All findings pertaining to hypothesis testing were identical to those presented below.
6 Preliminary analyses tested whether Poisson, zero-inflated Poisson (ZIP), negative binomial, or zero-inflated negative binomial regression was the most appropriate method of model estimation for each count dependent variable. Results showed the best fit for the negative binomial models. However, to differentiate between risk for using alcohol/drugs and risk for developing alcohol/drug problems among those who use alcohol/drugs, follow-up analyses predicted alcohol and drug problems using ZIP regression. Results were consistent with those presented below. PTSD symptoms had significant unique effects on risk for alcohol problems among those who drink (B = .10, p < .001, IRR: 1.11) and risk for drug problems among those who use drugs (B = .08, p < .001, IRR: 1.08). PTSD symptoms did not significantly influence the probability of being a nondrinker (B = −.02, p = .770, OR: .98) or the probability of being a nondrug user (B = −.08, p = .11, OR: .92). Therefore, these analyses PTSD symptoms significantly increase risk for alcohol and drug problems among those who use alcohol and drugs.
7 We did not believe it was advisable to assume that participants without trauma exposure had zero PTSD symptoms because it is feasible that nontrauma exposed individuals may have similar symptoms (e.g., sleep disturbances, irritability, feeling detached or estranged from others) that are not trauma-induced.
8 Males had more alcohol and drug problems than did females, whereas females had more PTSD symptoms than did males (see Table 1), thus obscuring the relation between PTSD symptoms and alcohol and drug problems.
9 To examine the robustness of this finding, additional analyses modeled the effects of past-year frequency of binge drinking, getting drunk, and using marijuana, on risk for trauma exposure or assaultive violence exposure. Similar to the main analyses, neither binge drinking nor getting drunk predicted overall risk for trauma exposure. However, binge drinking had a significant unique effect on risk for assaultive violence exposure over and above family adversity, gender, and ethnicity (B = 0.30, p = .032, OR = 1.34). Frequency of getting drunk and marijuana use had marginally significant unique effects on risk for assaultive violence exposure.
10 Additional analyses tested whether adolescent substance use problems or binge drinking interacted with two indices of trauma severity—number of traumatic events and type of trauma (assaultive violence exposure vs. other types of events)—to increase susceptibility to developing PTSD symptoms. There were no significant interactions.
11 Additional analyses tested whether PTSD symptoms mediated the influence of trauma severity (as indicated by type of trauma or number of traumas) on substance use problems among the 166 trauma-exposed participants. PTSD symptoms did not significantly mediate the effect of type of trauma, as type of trauma did not have a significant unique effect on risk for PTSD symptoms (B = 0.11, p = .35, IRR = 1.11). PTSD symptoms fully mediated the influence of number of traumas on risk for alcohol problems. For drug problems, the effect of PTSD symptoms on drug problems was marginally significant (B = 0.12, p = .057, IRR = 1.12) after accounting for the significant effect of number of traumas on drug problems (B = 0.57, p = .04, IRR = 1.76). In sum, these analyses supported the self-medication hypothesis, as PTSD symptoms influenced risk for alcohol and drug problems even when controlling for these indices of trauma severity.
12 Post hoc analyses tested the effects of adolescent substance use problems at either Wave 1, 2, or 3—whichever Wave was closest in time but preceding the traumatic event. For participants who were not exposed to trauma, Wave 3 substance use problems were used. Results were identical with respect to hypothesis testing to those presented in the manuscript, thus lending confidence that our findings are consistent even when substance use problems were assessed closer in time to the traumatic event.
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Submitted: April 26, 2013 Revised: October 18, 2013 Accepted: December 16, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (3), Sep, 2014 pp. 841-851)
Accession Number: 2014-24382-001
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Record: 52- Risk, compensatory, protective, and vulnerability factors related to youth gambling problems. Lussier, Isabelle D.; Derevensky, Jeffrey; Gupta, Rina; Vitaro, Frank; Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014 pp. 404-413. Publisher: American Psychological Association; [Journal Article] Abstract: This study explores the additive (i.e., risk or compensatory) or moderating (i.e., protective or exacerbating) role of individual resources (social bonding, personal competence, and social competence) and environmental risk (family, peers, and neighborhood) in regard to the association between established personal risk attributes (i.e., impulsivity, anxiety) and youth gambling problems. Using a cross-sectional design, regression analyses indicated that among a sample of mostly first-generation immigrant adolescents from low-income homes (N = 1,055; M = 15.03; SD = 1.64), social bonding was associated with a decrease in gambling problems (odds ratio [OR] = 0.15, p < .01) while peer and neighborhood risk were associated with an increase in gambling problems (OR = 2.24, p = .01 and OR = 2.31, p = .01, respectively), net of personal risk attributes. In terms of protective processes, no putative moderating effect was found for composite individual resources. The findings are discussed with respect to the roles of compensatory, risk, and protective processes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Risk, Compensatory, Protective, and Vulnerability Factors Related to Youth Gambling Problems
By: Isabelle D. Lussier
Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, Quebec, Canada;
Jeffrey Derevensky
Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, Quebec, Canada
Rina Gupta
Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, Quebec, Canada
Frank Vitaro
Department of Psychoeducation, University of Montreal, research unit on children’s psychosocial maladjustment, Montreal, Quebec, Canada
Acknowledgement: Isabelle D. Lussier is now at the Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada.
This research was supported by a studentship award from the Ontario Problem Gambling Research Centre and by a doctoral fellowship from the Fonds de recherche du Québec - Société et culture (FQRSC): Actions concertées: Les impacts socioéconomiques des jeux de hasard et d’argent.
Prevalence studies indicate that gambling among youth is widespread (Jacobs, 2004) and that opportunities to gamble are increasing with the expansion of land-based gambling, Internet gambling, interactive lotteries, mobile gambling, and novel slot machines (Griffiths & Wood, 2000). Gambling problems among youth are also relatively prevalent, with reported probable pathological gambling rates ranging between 2% to 7% among Canadian youth (Gupta & Derevensky, 2000; Martin, Gupta, & Derevensky, 2007). Adolescents with gambling problems are more likely to report difficulty in school and truancy (Ladouceur, Boudreault, Jacques, & Vitaro, 1999) as well as financial and personal problems (Burge, Pietrzak, & Petry, 2006; Schissel, 2001). Several studies have identified a number of risk factors that are predictively or concurrently associated with gambling problems in adolescents. These risk factors include personal risk attributes (e.g., disinhibition-impulsivity) and environmental factors (e.g., parenting). However, few studies have examined whether personal and environmental risk factors operate in a cumulative (i.e., additive) or a multiplicative (i.e., interactive) manner in regard to youth gambling problems. Therefore, the first goal of the current study was to determine the additive or interactive role of personal and environmental risk factors in relation to youth gambling problems.
Despite the well-established relationships between personal or environmental risk factors and gambling problems, many youth exposed to personal, environmental, or both risk factors never develop problem behaviors. This suggests the possibility for compensatory-resource factors or protective-moderating factors. Researchers in other areas of youth development have begun to identify factors that may counteract risk factors through a cancellation process in the case of compensatory factors or a mitigating-buffering process in the case of protective factors (Rutter, 1990). However, no study, to our knowledge, has examined this important issue in relation to youth gambling problems despite the important contribution such findings could have to the prevention literature for youth gambling problems. Therefore, the second goal of the current study was to determine whether individual resources could play a compensatory or protective role with respect to the link between risk factors and gambling problems.
A Multidimensional/Ecological Integrative PerspectiveInfluenced by Bronfenbrenner’s ecological (Bronfenbrenner, 1979) and bioecological (Bronfenbrenner, 2005) models, contemporary prevention research often emphasizes relationships and systems, and integrated biological, social, and cultural processes across time. Ecological theory is defined as:
. . . the scientific study of the progressive, mutual accommodation, throughout the life course, between an active, growing human being and the changing properties of the immediate settings in which the developing person lives, as this process is affected by the relations between these settings, and by the larger contexts in which the settings are embedded. (Bronfenbrenner, 2005, p. 107)
According to this model there are four environmental levels that can operate in an additive, mediating or interactive way. The four levels include the microsystem (actions and interactions within the environment that a child is behaving in at any given moment in time, e.g., home and school), the mesosystem (the interrelations among the child’s microsystems, e.g., the relationship between a child’s parents and school), the exosystem (environments that indirectly influence the child’s behavior and development, e.g., a parent’s workplace), and the macrosystem (broad social factors and cultural values that influence the other settings, e.g., social norms and public policies) (Bronfenbrenner, 2005; Kaminski & Stormshak, 2007). Most researchers now agree that a youth may be competent in one context or aspect of life but not another, and/or at one point in development but not another (O’Dougherty Wright & Masten, 2005). This wave of research has led to a search for processes related to positive outcomes (or the reduction or absence of negative outcomes), particularly in fields of substance use and delinquent behavior. This multidimensional framework was used to examine the possible cumulative or interactive role of risk factors across different levels (i.e., personal and environmental) as well as the possible compensatory or protective role of individual resources in regard to youth gambling problems.
Personal Risk Attributes Related to Youth Gambling ProblemsProblem gambling appears to be more prevalent among males than females (NRC, 1999). Males are more likely to gamble more money (Derevensky, Gupta, & Della–Cioppa, 1996), to begin gambling at an earlier age (Derevensky & Gupta, 2001), and to gamble more frequently (Jacobs, 2004). Attributes including physiological, personality, emotional, and coping variables have also been shown to be associated with excessive youth gambling behavior (Hardoon & Derevensky, 2002). Three personality variables often cited as predictors in youth gambling research include impulsivity, anxiety, and depression.
Predictive links have repeatedly been identified in longitudinal studies between poor impulse-control or behavioral disinhibition and youth problem gambling (Vitaro, Arseneault, & Tremblay, 1999; Wanner, Vitaro, Carbonneau, & Tremblay, 2009). In terms of anxiety, cross-sectional youth gambling research has identified a relationship between anxiety and youth problem gambling (Ste-Marie, Gupta, & Derevensky, 2006). However, this relationship may be less straightforward. For example, Vitaro and colleagues (1996) reported that low anxiety during childhood distinguished problem gamblers from non-problem gamblers in adolescence. In regards to depression, research findings have produced mixed results. Certain studies have cited that adolescents with gambling problems report higher levels of depression, suicide ideation, and suicide attempts (Lee, Storr, Ialongo, & Martins, 2011; Nower, Gupta, Blaszczynski, & Derevensky, 2004). However, recent longitudinal research has also demonstrated no significant link between childhood shy/depressed behavior and problem gambling in adulthood, again suggesting that additional factors may play a compensatory or a protective role (Shenassa, Paradis, Dolan, Wilhelm, & Buka, 2012). In turn, there are environmental factors that may also operate as risk factors, adding to or compounding the role of personal risk factors.
Environmental Risk Factors Related to Youth Gambling ProblemsAdolescent problem gamblers are more likely to have a parent who struggles with a gambling problem (Vachon, Vitaro, Wanner, & Tremblay, 2004), and often report having had their first gambling experience at home with a family member (Gupta & Derevensky, 1997). Youth gambling problems also appear to be linked to parental discipline, independent of parent gambling. For example, even after controlling for numerous variables (including socioeconomic status, gender, and impulsivity-hyperactivity problems), main effects were identified between youth gambling problems and poor parental monitoring and disciplinary strategies (Vachon et al., 2004).
Peer modeling and social learning also appear to be involved in the development of gambling problems (Gupta & Derevensky, 1997; Hardoon & Derevensky, 2001). For example, many adolescents report that they gamble because their friends do (Griffiths, 1990). Over time, adolescents with gambling problems have been reported to replace old friends with gambling associates (Gupta & Derevensky, 2000).
Although less research has directly examined predictive links between disadvantaged neighborhoods and gambling behavior, findings from a longitudinal study of boys living in economically deprived neighborhoods revealed an elevated risk of gambling problems among those whose mothers were below the median on maternal occupational prestige (Vitaro et al., 1999). To summarize, several studies reported an association, either longitudinally or concurrently, between personal or environmental factors and youth problem gambling. However, few studies have examined their possible cumulative or interactive role. Even fewer studies have examined whether resource factors could compensate or moderate the association between risk factors and youth problem gambling.
Compensatory and Protective Resource FactorsResource factors can operate in two distinct ways: By decreasing the chances of a negative outcome in the context of adversity (i.e., through a protective-moderating effect), and by decreasing the chances of a negative outcome regardless of exposure to adversity (i.e., through a compensatory-cancellation effect; Rose et al., 2004). In statistical terms, a compensatory factor implies a negative main effect (opposite to risk factors) whereas a protective factor implies a mitigating-buffering effect on the relationship between a risk variable and a maladaptive outcome. Only a small number of investigations have examined resource factors in relation to youth gambling behavior. A few studies examined whether resource factors such as social bonding, personal competence, and social competence played a compensatory role (Dickson, Derevensky, & Gupta, 2008; Lussier, Derevensky, Gupta, Bergevin, & Ellenbogen, 2007). To our knowledge, no study has examined the potentially protective role of these resource factors with respect to the relationship between personal or environmental risk factors and gambling problems. Indeed, these resource factors may serve to buffer youth that would otherwise be at risk of developing gambling problems by providing alternatives for engaging in, and by helping to avoid the negative consequences of excessive problem behavior.
Social Bonding
Social bonding represents the degree to which people feel a positive affect for, involvement with, and motivation toward success in social contexts (e.g., family and school) and acceptance toward conventional values (e.g., prosocial norms; Springer & Phillips, 1992). Studies have consistently denoted the importance of social bonds in relation to various high-risk behaviors (Resnick et al., 1997; Rutter, 1990), and more recently, to gambling problems (Dickson et al., 2008; Lussier et al., 2007). Two specific indicators of social bonding (i.e., family cohesion, and school connectedness) have been identified as compensatory mechanisms in relation to youth gambling problems (Dickson et al., 2008; Magoon & Ingersoll, 2006). As well, based on a large community sample (N = 1,273), using a cross-sectional design, Lussier and colleagues (2007) identified low social bonding to be the strongest predictor of youth gambling problems (over and above personal competence, social competence, family risk, neighborhood risk, and perceived deviant peers), while controlling for gender.
In terms of protective processes, no study has examined the potentially protective role of social bonding on the relationship between relevant risk factors and gambling problems. However, other high-risk behavior research has found school bonding to putatively moderate the relationship between deviant peers and substance use and deviant behavior (Crosnoe, Erickson, & Dornbusch, 2002). As well, family bonding has been found to putatively moderate the relationships between deviant peers and deviant behavior, alcohol, tobacco, and other drug use (Crosnoe et al., 2002) and between stress and low self-esteem and heavy episodic drinking (Jessor, Costa, Kruege, & Turbin, 2006).
Personal Competence
The ability to function effectively with a sense of purpose toward the future may be referred to as personal competence. Personal competence includes dimensions such as self-concept, self-control, self-efficacy, and positive outlook (Springer & Phillips, 1992). It has been identified as a compensatory factor in relation to youth gambling problems in a large cross sectional study (Lussier et al., 2007). However, when it was included in a larger model including other known predictors such as environmental risk and risky behaviors, it was not retained as a significant predictor. In terms of protective processes, no study has examined the potentially protective role of personal competence on the relationship between relevant risk factors and gambling problems. However, other high-risk behavior research has found self-control to putatively moderate the relationships between three predictors (family life events, adolescent life events, peer substance use) and substance use (Wills, Ainette, Stoolmiller, Gibbons, & Shinar, 2008), and self-efficacy beliefs have been found to moderate the relationships between home environment and social behavior, achievement, and overall problems (Bradley & Corwyn, 2001).
Social Competence
The definition of social competence varies among researchers. However, general themes include responsiveness, caring, and flexibility in social situations (Springer, Wright, & McCall, 1997); qualities that are believed to elicit positive responses from others. Three qualities related to social competence include assertiveness, confidence, and cooperation and contribution. Social competence has been investigated as a potential compensatory factor in relation to youth gambling problems (Lussier et al., 2007). Research on social competence among other high-risk behaviors has resulted in mixed results. Although some support exists for social competence as a compensatory mechanism (Sandstrom & Coie, 1999), other findings suggest that an inflated perception of social competence may actually increase externalizing behavior problems (Brendgen, Vitaro, Turgeon, Poulin, & Wanner, 2004; de Castro, Brendgen, van Boxtel, Vitaro, & Schaepers, 2007) and smoking and cannabis use (Veselska, Geckova, Orosova, van Dijk, & Reijneveld, 2009).
Socioeconomic Status (SES)Among many other factors, a child’s macrosystem and exosystem are partly defined by the socioeconomic status (SES) of the family. The term SES broadly refers to personal lifestyle variables including occupation, income, and education. Substantial research exists on the risk mechanisms involved between low SES and maladaptive outcomes. Several meta-analyses have summarized physical health issues, cognitive deficiencies, poor school achievement, and emotional and behavioral problems, including substance use, as effects of economic disadvantage on youth (McLoyd, 1998; Miech & Chilcoat, 2005). The data regarding the relationship between SES and gambling problems is both scarce and inconsistent. Some studies have found a positive association between low SES and gambling problems (Fisher, 1993; Schissel, 2001). Findings from a prospective longitudinal study of boys living in economically deprived neighborhoods, found that those whose mothers were below the median on maternal occupational prestige were significantly more at risk of gambling problems. That is, the poorest adolescent males were at greatest risk (Vitaro et al., 1999). Other studies reveal a more complex relationship between SES and youth gambling problems. For example, a study by Welte and colleagues (2008) reported that although low SES individuals as a whole reported less gambling activity in the past year, those that did indicate having gambled were more likely to meet the criteria for problem gambling. As well, a study by Auger and colleagues (2010), demonstrated that low SES influenced gambling onset primarily among impulsive youth, identifying impulsivity as a risk factor for gambling onset among low but not high SES youth. Because of the uncertainty with respect to the role of SES in reference to gambling problems, and given our interest in protective and compensatory factors, we selected a naturally occurring homogeneous high-risk sample of low SES adolescents. Hence, SES was methodologically controlled.
Current ResearchThe current study was designed to (1) identify whether personal risk attributes (gender, impulsivity, and emotional problems) and environmental risk factors (family, peers, and neighborhood) operate additively or interactively in the prediction of gambling problems in a sample of low SES adolescents (2) identify whether individual resources (social bonding, personal competence, and social competence) operate as compensatory or protective factors in the prediction of youth gambling problems.
Method Participants
The sample included 1,055 participants (535 males, 518 females, 2 were missing gender information) in Grades 7 to 11 (ages 11–18; M = 15.03; SD = 1.64) from three schools in the Montreal area, with an overrepresentation of students from low-income homes (see Table 1). Schools were targeted using the Classification des écoles primaires et classification des écoles secondaires (CES; ranking of 1–27 out of 90; CGTSIM, 2006) and by the Indices de défavorisation par école [decile ranking of 8 to 10 on the low-income cutoff (LICO); MELS, 2006]. The CES is an annual classification system that hierarchically classifies schools according to the proportion of students from underprivileged homes, whereas the Indices de défavorisation par école is a broader school population map that covers the province of Quebec, and classifies schools by decile rankings for two indices, one of which denotes a low-income cutoff. Notably, the student body of the school from which the majority of data was collected (n = 813) was largely made up of first-generation immigrant youth. In fact, only 38% of the school’s student body (at the time of data collection) were born in North America. Next to Quebec (36%), the birthplace for most students in the school was the Republic of China (13.7%).
Distribution by Sex and Developmental Level
Instruments
Outcome variable: Gambling
The Gambling Activities Questionnaire (GAQ; Gupta & Derevensky, 1996) was used to identify Non-Gamblers (failure to endorse any of the 12 gambling activities during the past year). The Diagnostic and Statistical Manual for Mental Disorders–Fourth Edition–Multiple Response - Juvenile (DSM–IV-MR–J) (Fisher, 2000) was used to assess severity of problem gambling among those gambling. Gamblers were classified into three groups; Social, At-Risk, or Probable Pathological. A score of 0 or 1 was indicative of social gambling, a score of 2 or 3 reflected an at-risk level of gambling, and a score of 4 or more was indicative of probable pathological gambling (PPG). The internal consistency reliability for these scales was adequate, with Cronbach’s α = .72 and .75, respectively. French versions of these measures have been previously used in prior research (Dickson et al., 2008).
Individual resources
The Individual Protective Factors Index (IPFI) was designed to assess 61 individual resources on a 4-point Likert scale across three domains; Social Bonding (family bonding, school bonding, and prosocial norms), Personal Competence (self-concept, self-control, positive outlook, and self-efficacy), and Social Competence (assertiveness, confidence, and cooperation and contribution) (Springer & Phillips, 1992).
Social bonding
This domain is comprised of 18 questions distributed evenly across the three dimensions of school bonding (e.g., “Finishing high school is important”), family bonding (e.g., “I can tell my parents the way I feel about things”), and prosocial norms (e.g., “I like to see other people happy”). The social bonding domain score was computed by adding all raw social bonding subscale scores together and dividing by the total number of social bonding items, thus providing a domain score with a range of 1–4. The internal consistency reliability for this scale was adequate, with Cronbach’s α = .77.
Personal competence
The focus of this domain (consisting of 25 questions) is on individual identity, relating to one’s sense of personal development, self-image, and outlook on life (Springer & Phillips, 1992). The four dimensions within this domain include self-concept (e.g., “I like the way I act”), self-control (e.g., “When I am mad, I yell at people”), self-efficacy (e.g., “Other people decide what happens to me”), and positive outlook (e.g., “I am afraid my life will be unhappy”). The personal competence domain score was computed by adding all raw personal competence subscale scores together and dividing by the total number of personal competence items, thus providing a domain score with a range of 1–4. The internal consistency reliability for this scale was adequate, with Cronbach’s α = .79.
Social competence
The elements of this domain include one’s ability to feel responsive, caring, and flexible in social situations. Eighteen questions in this domain are distributed evenly across three subscales including assertiveness (e.g., “If I don’t understand something, I will ask for an explanation”), confidence (e.g., “I will always have friends”), and cooperation/contribution (e.g., “I always like to do my part”). The social competence domain score was computed by adding all raw social competence subscale scores together and dividing by the total number of social competence items, thus providing a domain score with a range of 1–4. The internal consistency reliability for this scale was good, with Cronbach’s α = .80.
For the purpose of this study, the three dimensions of individual resources (Social Bonding, Personal Competence, and Social Competence) were considered separately as potential compensatory factors. In addition, a composite score was created to reflect a global individual resources score. The reason for this composite score was to test the global protective (i.e., moderating) effect of the individual resources in a parsimonious manner. To create a composite-global score, all 10 subscale scores were summed together and divided by the total number of items, as per manual guidelines. The internal consistency reliability for this scale was excellent, with Cronbach’s α = .90.
Personal and Environmental Risk Factors
Impulsivity
Impulsivity was assessed by using the five impulsiveness items from the Eysenck Impulsiveness Scale (EIS) with the highest factor loadings on the original scales (e.g., “Do you generally do and say things without stopping to think?”; Eysenck, Easting, & Pearsons, 1984). All items required yes/no responses and were summed to create a composite score ranging from 0–5. The internal consistency reliability for the current sample was adequate (Cronbach’s α = .76).
Anxiety
The Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1998) includes 21 items on a 4-point Likert scale, with scores ranging from 0–3 (e.g., “Indicate how much you have been nervous during the past week”). The composite score ranges from 0–63. Although the BAI was initially designed for adults, it has been shown to have acceptable psychometric properties among adolescents (Osman et al., 2002). The internal consistency reliability for the current sample was excellent, with Cronbach’s α = .91.
Depression
The Reynolds Adolescent Depression Scale–2nd Edition (RADS–2) was used to assess depressive symptomatology. The 30 items (e.g., “I feel lonely.”) are rated on a 4-point Likert scale, with composite scores ranging from 30–120 (Reynolds, 2002). The internal consistency reliability for the current sample was excellent, with Cronbach’s α = .93.
Environmental risk
Part two of the IPFI, the EMT-Risk, scores 25 environmental risk questions across six subscales (Springer & Phillips, 1992). Scores were calculated such that high scores reflected greater risk in the domains of Family (supervision [e.g., “The rules in our house are clear”] and interaction [e.g., “You talk to your parents about school”]), Peers (positive peer associations [e.g., “Your closest friends study hard at school”], peer AOD use exposure [e.g., “Your closest friends try drugs like marijuana or cocaine once in a while”]), and Environment (neighborhood environmental risk [e.g., “You see the police arrest someone”] and AOD use exposure [e.g., “You have been around other kids who were drinking alcohol”]). For the purpose of this study, the three risk dimensions of Family, Peers, and Environment were considered separately as potential additional risk factors. In addition, a composite score was created to test their global exacerbating (i.e., moderating) effect. The reason for a composite environmental risk score was to test its global exacerbating (i.e., moderating) effect in a parsimonious manner. To create a composite-global score, all six subscale scores were summed together and divided by the total number of items. EMT-Risk items were rated on a 2-, 3-, or 4-point scale, with composite scores ranging from 1–3.28. Although the EMT-Risk is not standardized, it has demonstrated excellent internal consistency reliability (Cronbach’s α = .91) in prior research using a large sample (Lussier et al., 2007). The internal consistency reliability for the present sample was good, with Cronbach’s α = .80.
Procedure
Data collection was group administered in classrooms or school cafeterias. Students completed the survey in one 50-min period. Consent was obtained from parents and adolescents before their participation. Teachers remained at the front of the room to respect participants’ confidentiality. Questionnaires that were deemed unreliable (e.g., zigzag or patterned responses, illegible responses, or questionable information) were discarded (n = 58).
Data Screening
Two participants were missing more than 10% of responses for the DSM–IV-MR-J. Because this measure was one of the two instruments used to create the main criterion variable, their scores were excluded from the data set. The other instrument that was used to create the gambling criterion variable was the GAQ. No student omitted more than 10% of items on the GAQ. Missing data analysis for this instrument was simplified by the fact that it was used as a filter variable to discriminate those who gambled from those who did not. Participants that endorsed no gambling activities in the past year were classified within the Non-Gambling category. For all other instruments, if a participant failed to respond to >10% of items, their scores were excluded from further analyses pertaining to that instrument. Administration manuals were consulted to address missing values within acceptable limits (<10%). For measures with no suggested protocol, items with significantly skewed distributions were imputed with item medians, whereas items with nonsignificantly skewed distributions were imputed with item means.
Outliers were identified by transforming each instrument composite variable into z-scores. Scores that were >3 SD were converted to a value that equaled the next most extreme score within 3 SD. Bivariate multicollinearity was assessed using a correlation matrix. No pairwise correlations exceeded r = .64, indicating no evidence of multicollinearity. Multivariate correlations were assessed by running a linear regression with the DSM–IV-MR-J as the dependent variable, and all other variables as independent variables. Collinearity diagnostics revealed no Tolerance statistic value <.20 and no VIF value >10, suggesting no evidence of multivariate singularity or multicollinearity. The DSM–IV-MR-J instrument was significantly positively skewed, with a skewness value = 2.04. Logarithmic, square root, and inverse function transformations were unable to significantly improve the shape of the distribution. Consequently, this variable was left as nonnormal and analyzed categorically wherever possible.
Statistical Analyses
Sequential binary logistic regressions were used to determine the combination of personal risk attributes (impulsivity, anxiety, and depression) and environmental risk (family, peers, and neighborhood) that best predict problem gambling and to explore the possible compensatory or interaction effects of individual resources (social bonding, personal competence, social competence) with respect to these two categories of risk factors. The outcome variable was coded as 0 = Non-Gambler or Social-Gambler and 1 = At-Risk or PPG (see details later). Gender and age were considered as potential control variables, and the personal risk attributes of impulsivity, anxiety, and depression were considered as potential predictor variables. However, age and depression were not significantly related to problem gambling and as such were removed from further models. In the first series of regressions, gender, anxiety, and impulsivity were entered at Step 1 and environmental risk domains and/or individual resource domains were entered at Step 2 to test for their respective additive risk or compensatory role. In the second series of regressions, gender, anxiety, and impulsivity were again entered at Step 1 and composite individual resource scores, composite environmental risk scores, and interaction terms were entered at Steps 2, 3, 4, and 5 to explore the potentially putative buffering and exacerbating moderating effects of individual resources and environmental risk on the relationships between the personal risk attributes (impulsivity and anxiety personality variables) and youth gambling problems, while controlling for other known predictors (gender). For all regressions, the Hosmer and Lemeshow test was nonsignificant, indicating an adequate model fit. Collinearity diagnostics revealed no multicollinearity among the examined variables as ascertained by Tolerance and Variance Inflation Factor statistics. Furthermore, tests for outliers revealed only one case with a z-residual score greater than three, which is considered to be acceptable in analyses involving a large sample.
ResultsA majority (60.2%) of respondents gambled at least once in the past year. The most endorsed activities were cards, sports betting, scratch tickets, poker, and bingo. Overall, 39.8% of participants were non gamblers (n = 419; 35.1% male; 44.8% female); 49.6% were social gamblers (score of 0 or 1 on the DSM–IV-MR-J; n = 522; 49.5% male; 49.4% female); 7.9% met the criteria for At-Risk gambling (score of 2 or 3; n = 83; 11.1% male; 4.6% female); and 2.8% met the criteria for PPG (score of 4 or more; n = 29; 4.3% male; 1.2% female). Overall, 10.7% (n = 112) of the sample indicated some form of gambling related problem. As expected, gender differences were pronounced with males reporting more gambling-related problems than females, (χ2 [1, N = 1,051] = 29.47, p < .001). Differences between age groups were not significant (χ2 [9, N = 1,050] = 15.45, p = .079).
Given the inherent limitations of the cross-sectional design of this study, findings only represent correlational associations that may not be interpreted in terms of temporal or causal links. However, for simplicity terms such as predict or predictor are used to refer to main effects.
The first regressions consisting of gender, impulsivity, and anxiety in Step 1, and the three environmental risk domains (Family, Peer Group, and Neighborhood) entered in Step 2, revealed that Peer Group (z = 6.35, p = .01) and Neighborhood (z = 6.76, p = .01) were retained in the model according to the established Wald criterion but Family was not. In addition, impulsivity was related to problem gambling, net of participants’ gender and environmental factors. The second set of regressions consisting of gender, impulsivity, and anxiety in Step 1, and the three individual resources (Social Bonding, Social Competence, and Personal Competence) in Step 2, revealed that the Social Bonding domain was the only resource to be retained in the model (z = 20.96, p < .001). In a third set of regressions, all six environmental risk and individual resource domains were entered into a prediction model to test for their unique additive or compensatory role. As presented in Table 2, the Peer Group, Neighborhood, and Social Bonding domains were again retained, above and beyond the effects of other known contributors, including gender, impulsivity, and anxiety (z = 4.47, p < .05; z = 4.53, p < .05; and z = 7.24, p < .01, respectively). The Family, Social Competence, and Personal Competence domains were again excluded, as they still did not improve the prediction of problem gambling.
Sequential Logistic Regressions of Domains Predicting Problem Gamblers
To explore the possibility of interaction effects, a conceptual model was tested in which environmental risk and individual resources were assumed to respectively exacerbate and buffer the relationship between either impulsivity or anxiety and problem gambling. As well, two three-way interactions between individual resources, personal risk attributes, and environmental risk were anticipated, such that individual resources would be particularly protective in a context where one or the other of the personal risk attributes and global environmental risk conspire interactively in predicting problem gambling. However, no interaction terms were retained in the final prediction model (see Table 3).
Sequential Logistic Regression Models for Problem/Non Problem Gambling Groups
DiscussionBased on a sample of adolescents deriving mostly from low income homes, analyses identified social bonding as a compensatory factor and peer and neighborhood risk as additional salient risk factors in the prediction of youth gambling problems, net of personal risk attributes such as impulsivity and gender, which also made significant contributions. Of all six environmental risk (family, peers, and neighborhood) and individual resource (social bonding, personal competence, and social competence) variables, low social bonding emerged as the strongest predictor of problem gambling, followed by neighborhood and peer environmental risk. No moderating role was identified for global individual resource or global environmental risk scores on the relationships between personal risk attributes (impulsivity and anxiety) and youth gambling problems. As well, the two three-way interaction terms between either personal risk attribute, global environmental risk, and global individual resources were not significant. These results are discussed in turn, after addressing a number of preliminary issues.
Preliminary Issues
Problem gamblers made up 10.7% of the current sample (7.9% At-Risk; 2.8% PPGs). Several Canadian studies have reported PPG rates of adolescents being 3% to 7% (Derevensky & Gupta, 2000; Gupta & Derevensky, 2000), although more recently, lower rates have also been reported (Martin et al., 2007 [2.1%]) The lower rate of PPGs in this sample suggests that youth from low-income homes do not appear to be at increased risk for developing gambling problems. However, the total rate of a little more than 10% of adolescents in the present sample with at least one gambling-related problem is a matter for concern.
As anticipated, impulsivity was identified as a significant predictor of problem gambling. Anxiety was also a predictor of problem gambling but in a less consistent way. However, depression was not. This finding corroborates recent longitudinal research whereby children that exhibited impulsive behavior at age seven were more than three times more likely to report problem gambling in adulthood. However, no significant link was found between childhood shy/depressed behavior and emerging gambling behavior in childhood (Vitaro & Wanner, 2011) or problem gambling in adulthood (Shenassa et al., 2012).
Individual Resources
As anticipated, social bonding was identified as a compensatory mechanism, contributing to the prediction models of gambling problems over and above other known predictors (gender, age, impulsivity, anxiety, and depression). This finding replicates similar findings from other studies that have sought to identify compensatory factors related to youth gambling problems (Dickson et al., 2008; Lussier et al., 2007). Possible mechanisms that could help explain the compensatory effect of social bonding may relate to social control theory, which emphasizes the importance of internalized attachments to conventional role models (e.g., parents), and bonds to institutions (e.g., school) and individuals who discourage maladaptive behavior (Petraitis, Flay, & Miller, 1995). In terms of protective processes, no interaction term was significant in the binary logistic regression models for gambling problems. However, logistic regressions are known to be an insensitive test for such effects (Preacher, MacCallum, Rucker, & Nicewander, 2005), and the power for detecting such differences may be reduced if sample sizes are highly unequal (Fleiss, Tytum, & Ury, 1980), as was the case for the gambling variable in the current study.
Environmental Risk
As anticipated, the neighborhood and peer group variables were identified as important risk factors for gambling problems over and above other known predictors. The link between peer risk and youth gambling behavior supports prior findings that peer modeling and social learning are involved in the onset of gambling problems (Hardoon & Derevensky, 2001). Many adolescents report that they gamble because their peers gamble. The exact mechanisms that could help explain this association, however, remain unclear because both socialization and selection effects can be involved in a cross-sectional study as ours. However, one may suspect modeling and social reinforcement effects, in addition to selection effects of gambling peers.
Although very little research has been conducted regarding the relationship between neighborhood risk and youth gambling behavior, existing literature corroborates the present findings (Lussier et al., 2007). The contribution of neighborhood risk is particularly important in the context of our study given the likely restricted range on this variable as a consequence of our low socioeconomic status sample. Future investigation into this risk factor may help to better understand its relation to the development of youth gambling problems.
Adversity Among Adolescents in the Present Sample
In an attempt to procure a naturally occurring high-risk sample (youth from low income homes), the classification systems of two separate government organizations were consulted to identify schools consisting primarily of youth from low-income homes. Although this population is widely cited as being at high-risk for numerous maladaptive outcomes, the present sample did not appear to engage in elevated levels of gambling behavior. Further, the normal distribution and adequate variability of environmental risk in this sample (M = 1.90, SD = 0.30), as well as significant mean differences in environmental risk among gambling groups provided little justification for considering the present sample “high-risk.”
Of note was that most students in the current sample were first-generation immigrant youth. Although there is little research regarding first-generation immigrant youth, existing literature indicates that parenting practices among Chinese and Latino immigrant parents may place greater emphasis on parental control (Chao, 1994; Domenech Rodríguez, Donovick, & Crowley, 2009). Another consequence of this feature of our sample may have been a reduced variability on the Family risk scale, thus explaining its noncontribution in the analyses.
Implications
The utility of research on compensatory and protective processes lies in its assimilation into prevention programs and subsequent evaluation regarding program efficacy. Environmental micro-, meso-, exo-, and macro-systems should be taken into consideration in the design, implementation, and evaluation stages of such initiatives as these four systems are increasingly recognized as important transactional variables that significantly influence human development (Bronfenbrenner, 2005; Kaminski & Stormshak, 2007). Prevention programs geared toward concurrently fostering compensatory factors, particularly social bonding, and reducing risk factors, particularly neighborhood and peer environmental risk may lead to lower levels of problem gambling, although prospective longitudinal research would be required to confirm these findings.
Limitations of the Current Study and Future Directions
Mean differences in the risk exposure and individual resource scales among gambling groups were in the anticipated directions. However, differences were small in scale, with greatest scaled score difference being only .39 for environmental risk and .22 for individual resources (both between Non-Gamblers and PPGs). Although, this may be due in part to the large sample size (N = 1,053), this finding alone may not translate into practical implications for prevention and intervention efforts. As well, all measures in this study were self-report. As such, correlations may be inflated because of shared method variance. The generalizability of the results of this study is limited by the fact that the majority of participants were derived from low-income homes and that most students in the current sample were first-generation immigrant youth. The EMT-Risk scale is not standardized and future research should consider a standardized measure of this scale. Because this sample does not appear to have been exposed to significant adversity, additional research with a naturally occurring high-risk sample could provide a better a understanding of how risk and vulnerability factors lead to gambling problems in certain youth and not in others and to what extent unique compensatory and protective factors can be identified. Finally, the design of this study was cross-sectional, preventing causal interpretations of the relationships between environmental risk, personal risk attributes, and individual resources, on one hand, and gambling problems on the other hand. Replication of the findings from the present research, as well as prospective longitudinal research is required to determine causal links and to investigate how the relationships between these variables develop over time.
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Submitted: October 12, 2012 Revised: July 12, 2013 Accepted: July 15, 2013
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Record: 53- Self-injury, substance use, and associated risk factors in a multi-campus probability sample of college students. Serras, Alisha; Saules, Karen K.; Cranford, James A.; Eisenberg, Daniel; Psychology of Addictive Behaviors, Vol 24(1), Mar, 2010 pp. 119-128. Publisher: American Psychological Association; [Journal Article] Abstract: This research examined two questions: (1) What is the prevalence of self-injurious behavior (SIB) among college students, overall and by gender, academic level, and sexual orientation? (2) To what extent is SIB associated with different forms of substance use and other risk behaviors? A probability sample of 5,689 students completed an Internet survey on self-injury, mental health, and substance use. Past-year prevalence of SIB was 14.3%, with undergraduates significantly more likely than graduate students to engage in SIB. Drug use and frequent binge drinking were associated with higher rates of SIB. Among those who engaged in any SIB, those who used drugs had higher depression scores, higher prevalence of cigarette smoking, and higher rates of binge eating. In a multiple logistic regression model predicting SIB, depression, cigarette smoking, gambling, and drug use were significant predictors. Information about those at risk for SIB is critical for the design of prevention and intervention efforts as colleges continue to grapple with risky behaviors. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Self-Injury, Substance Use, and Associated Risk Factors in a Multi-Campus Probability Sample of College Students
By: Alisha Serras
Department of Psychology, Eastern Michigan University;
Karen K. Saules
Department of Psychology, Eastern Michigan University
James A. Cranford
Department of Psychiatry, University of Michigan
Daniel Eisenberg
School of Public Health, University of Michigan
Acknowledgement: The data collection for this study was supported by the University of Michigan Depression Center; the Penn State Children, Youth, and Families Consortium; and the universities participating in the study. The first author was supported by the Eastern Michigan University Department of Psychology and the EMU Graduate School. Data presented in this manuscript were included in a preliminary report presented at the 2009 American Psychological Association Convention, August 2009, Toronto.
Self-injury, defined as the infliction of physical harm to one’s body without suicidal intent (Simeon & Favazza, 2001), has become a dangerous trend on college campuses (Whitlock, Lader, & Conterio, 2006). Types of self-injury include, but are not limited to, the following: cutting oneself, burning oneself, scratching oneself, pulling one’s hair, biting oneself, interference with one’s wound healing, carving into one’s skin, rubbing sharp objects into one’s skin, and punching object(s) to inflict bodily self-harm (Gratz, 2001). Despite the growing interest in self-injury, assessment tools to measure self-injurious behavior (SIB) remain in their infancy (Gratz, 2001; Whitlock & Knox, 2007). Historically, research on SIB has focused on borderline personality disorder and developmental disorders. More recently, SIB has begun to manifest in subclinical or nonclinical populations, including college students (Whitlock, Muehlenkamp, & Eckenrode, 2008). Several reasons to be specifically concerned about SIB are increased morbidity because of medical complications, infections, and scarring (Plante, 2006); deficits in emotional regulation (e.g. Klonsky, 2009; Hasking, Momeni, Swannell, & Chia, 2008; Ross, Heath, & Toste, 2009); and the increased risk of suicide (e.g. Prinstein et al., 2008; Whitlock & Knox, 2007).
Self-injury has also been linked with other risky behaviors, such as disordered eating, substance use, and suicide (Haw, Hawton, Casey, Bale, & Shepard, 2005; Ross et al., 2009). The extent to which associations between SIB and risk behavior vary by gender and academic level, however, has not been studied; the present study aims to address this gap. Below we briefly review the literature on SIBs and the associations between self-injury, alcohol and drug use, disordered eating, gambling, and depression, with a focus on studies of college students.
Academic Status and Self-InjuryRecent research suggests that the prevalence of SIB in college students is alarmingly high, with 7% of students reporting past-month SIB (Gollust, Eisenberg, & Golberstein, 2008), and 17% to 38% reporting any self-injury over their lifetime (Gratz, Conrad, & Roemer, 2002; Whitlock, Eckenrode, & Silverman, 2006). Because of the high rates of SIB on college campuses, it is important to understand demographically who is at risk for this behavior so that prevention and intervention efforts can be tailored to those specifically experiencing this problem. While numerous studies have focused on undergraduate students, few have examined the prevalence and co-occurrence of substance use behaviors and self-injury in graduate students. Earlier reports (Jacobson & Gould, 2007) suggested that rates of SIB appear to decline with age. By contrast, Whitlock et al. (2008) reported comparable SIB rates for graduate and undergraduate samples, although the sample was limited to people age 24 or younger.
Gender and Self-InjuryAssociations between gender and the prevalence of SIB among university students are still unclear. Gollust and colleagues (2008) and Heath, Toste, Nedecheva, and Charlebois (2008) reported no significant gender differences in prevalence of self-injury. By contrast, Whitlock, Eckenrode, and Silverman (2006) reported that females engage in repetitive SIB significantly more than males. Moreover, Hawton and Harriss (2008) reported that, in a clinical population, females engaged in SIB more frequently than males (60% and 40%, respectively). A review conducted on extant literature concluded that differences based on gender remain inconclusive (Jacobson & Gould, 2007). Given such discrepant reports, the present study aims to add to our understanding of the relationship between gender and SIB.
Sexual Orientation and Self-InjuryAnother demographic characteristic that has been related to SIB is sexual orientation. Studies have shown that those who endorsed a sexual orientation of bisexual, lesbian, questioning their sexuality, or gay reported higher rates of SIB (e.g. Deliberto & Nock, 2008; Gollust et al. 2008; Whitlock, Eckenrode, & Silverman, 2006). It is important to understand the relationship between sexual orientation and SIB so that universities can properly target these vulnerable groups and reach out to them effectively. Because of the extant literature on the relationship between sexual orientation and SIB, we would expect to replicate the finding of higher rates of SIB among nonheterosexual participants.
Alcohol Use and Self-InjuryBinge drinking among college students has been identified as a major public health problem (Ham & Hope, 2003; Hingson, Heeren, Winter, & Wechsler, 2005). Compared with their noncollege-attending peers, college students have higher rates of past-month alcohol use (Substance Abuse and Mental Health Services Administration [SAMHSA], 2007), binge drinking (Dawson, Grant, Stinson, & Chou, 2004; SAMHSA, 2007; Slutske, 2005; Slutske et al., 2004), and alcohol abuse (but not dependence; Slutske, 2005).
The National Institute on Alcohol Abuse and Alcoholism (NIAAA) defines “a ’binge’ as a pattern of drinking alcohol that brings blood alcohol concentration (BAC) to 0.08 gram-percent or above. For a typical adult, this pattern corresponds to consuming 5 or more drinks (male), or 4 or more drinks (female), in about 2 hours” (http://www.collegedrinkingprevention.gov/1College_Bulletin-508_361C4E.pdf, p. 2, retrieved April 13, 2009). While the 5/4 cutoff measurement has been shown to distinguish some drinkers, it does not distinguish between all drinkers, across all indices (Read, Beattie, Chamberlain, & Merrill, 2008). Furthermore, the 5/4 rule has been criticized as being imprecise because of the exclusion of weight, duration of drinking episode, and pharmacokinetics (NIAAA).
Conversely, research has also indicated that binge drinking may not be associated with mental health problems, perhaps because of the socially normative aspect of binge drinking on college campuses. For example, Cranford, Eisenberg, and Serras (2009) found that major depression was associated with lower odds of frequent binge drinking. Furthermore, results from the NESARC (Dawson, Grant, Stinson, & Chou, 2005) indicated a direct association between major depression and binge drinking among noncollege students but not among college students.
Despite research on the associations between alcohol use and mental health, the relationship between alcohol use and self-injury is largely understudied, and the available literature reports varied findings. For example, Gollust and colleagues (2008) reported no significant relationship between binge drinking and SIB. This study, however, did not account for the variation in frequency of binge drinking in relation to SIB or the effects of drug use (other than marijuana) and binge drinking on SIB. Other studies indicate that as many as one-third to nearly one-half of SIBs occur within 6 hours of alcohol intake (Haw et al., 2005; Hawton & Harriss, 2007; Hawton et al., 2003). In addition, a study on adolescents found that alcohol abuse was a risk factor for SIB (Deliberto & Nock, 2008).
It is unclear whether the association between binge drinking and SIB is a function of the quantity of alcohol consumed or the concurrent drug use that commonly accompanies excessive alcohol use (O’Grady, Arria, Fitzelle, & Wish, 2008). The present study aims to clarify the extent to which each of these variables confers risk for SIB. Based on the literature, we hypothesized that a positive relationship between binge drinking and SIB would exist, and that this association would not vary by gender (see Jacobson & Gould, 2007) but would vary by academic level.
Drug Use and Self-InjuryRelatively high prevalence rates of substance use behaviors such as tobacco and marijuana use have been well documented among undergraduate students (American College Health Association, 2007; Mohler-Kuo, Lee, & Wechsler, 2003; Wechsler et al., 2002). Self-injury is common in clinical samples of substance dependent persons, with prevalence rates reported between 34% and 50% (Evren & Evren, 2005; Oyefeso, Brown, Chiang, & Clancy, 2008). Furthermore, Deliberto and Nock (2008) reported that drug abuse was a significant risk factor for SIB by adolescents. However, the extent to which various forms of substance use behaviors are associated with SIB among college students is not well understood. In the aforementioned paragraph it is noted that frequent binge drinking is associated with increased drug use (O’Grady et al., 2008). In this report, we aim to clarify the relative risk of SIB conferred by illicit drug use versus frequent binge drinking.
Smoking and Self-InjuryNumerous studies have documented linkages between nicotine dependence and mood and anxiety disorders (Breslau, 1995; Breslau, Kilbey, & Andreski, 1991; Grant, Hasin, Chou, Stinson, & Dawson, 2004; Hagman, Delnevo, Hrywna, & Williams, 2008; see Morissette, Tull, Gulliver, Kamholz, & Zimering, 2007, for a review of research on anxiety and smoking). Evidence for associations between cigarette smoking and symptoms of anxiety and depression in college samples has also been reported (Lenz, 2004; Saules et al., 2004; for a review, see Patterson, Lerman, Kaufmann, Neuner, & Audrain-McGovern, 2004). Although the literature is sparse in this area, recent research indicates that self-injurers report higher prevalence rates of smoking than their noninjuring peers (Gollust et al., 2008; Matusmoto & Imamura, 2008). Furthermore, Jacobson and Gould (2007) reported that correlates of SIB among adolescents include a history of smoking. Because of the research suggesting a relationship between negative affect and self-injury and the research on adolescent smoking and prevalence of SIB, we hypothesized that the rates of SIB would be higher among college students who smoke cigarettes than among those who do not.
Disordered Eating and Self-InjuryEmotional regulation is defined as the way people manage and manipulate their emotions so that they remain consistent with their goals and objectives (Selby, Anetis, & Joiner, 2008). Emotional dysregulation is maladaptive emotional regulation. Self-injury and disordered eating have been established as ways that people cope with emotional dysregulation (Muehlenkamp et al., 2008). These behaviors are initiated in an attempt to decrease negative affect (Selby et al., 2008). However, despite research indicating the co-occurrence of self-injury and disordered eating, few studies have examined this relationship in college students. The literature for college students indicates that between 25.9% and 28.1% of those who self-injure also engage in disordered eating behavior (Gollust et al., 2008; Whitlock, Eckenrode, & Silverman, 2006). We hypothesized that rates of SIB would be higher in students who engage in disordered eating behavior; specifically, in this database, we assessed binge eating.
Gambling and Self-InjurySelf-injury and gambling have both been linked to impulse control problems, addictive behaviors, and obsessive-compulsive spectrum disorders, but the extent to which this comorbidity occurs in college students is unknown (Lochner & Stein, 2006). Furthermore, gambling has been linked to other risky behaviors such as excessive drinking, drug use, and binge eating (Engwall, Hunter, & Steinberg, 2004). These other behaviors also overlap with self-injury, and therefore we hypothesized that SIB would be associated with gambling behavior.
Depression and Self-InjuryResearch has shown that self-injury is associated with negative affect and emotional dysregulation. For example, Gollust et al., (2008) found that, among college students who self-injure, 32.5% screened positive for a probable depressive disorder. Furthermore, Briere and Gil (1998) reported that the two most common reasons people self-injured were “to distract themselves from painful feelings” and “to punish themselves” (p. 615). In addition, they reported that people engaged in SIB because they thought it would reduce their emotional pain. Therefore, we hypothesized that higher depression scores would be associated with greater likelihood of SIB.
SummarySelf-injury and substance use among college students have separately received substantial attention from researchers, but few studies have systematically evaluated the associations between these two behaviors. Understanding the association between SIB and substance use is important due to the possibility of increased lethality of SIB while under the influence.
Accordingly, the present study addressed two primary questions: (1) What is the prevalence of self-injury among college students, overall and by gender, academic level (undergraduate vs. graduate status), and sexual orientation? (2) To what extent is self-injury associated with different forms of substance use (cigarette smoking, binge drinking, illicit drug use) and other risk behaviors (gambling, binge eating)?
Method Participants
Our sample was based on an Internet survey of students attending 13 universities across the United States. We randomly selected 1,000 students at each university, or 13,000 total (76.5% undergraduates, 23.5% graduate or professional students), from a database of all enrolled students who were at least 18 years old. These students were sent mail and e-mail invitations to complete the survey on a secure Web site. After reading a description of the study, participants indicated their consent by clicking on the link to begin the survey. The study was approved by each university’s Health Sciences Institutional Review Board.
Accounting for Nonresponse Bias
A total of 5,689 students provided survey data, for a response rate of 44%. We constructed response propensity weights to adjust for differences between respondents and nonrespondents. These weights were equal to one divided by the predicted probability of response, which was estimated using multiple logistic regressions and administrative data on the following characteristics of all students randomly selected for the study: gender, race/ethnicity, year in school, international student status, and grade point average (GPA).
Measures
Self-injury
One question, developed for this study, assessed self-injury in the past year (Gollust et al., 2008). The item asked about the most common forms of SIBs, and was worded as follows: “This question asks about ways you may have hurt yourself on purpose, without intending to kill yourself. In the past year, have you ever done any of the following intentionally? (Select all that apply.)” Response options were: (1) cut myself, (2) burned myself, (3) banged my head or other body part, (4) scratched myself, (5) punched myself, (6) pulled my hair, (7) bit myself, (8) interfered with wound healing, (9) carved words or symbols into skin, (10) rubbed sharp objects into skin, (11) punched or banged an object to hurt myself, or (12) “other” (specify). If respondents specified behaviors exclusively in the “other” category, which were not consistent with self-injury as the deliberate and direct destruction of body tissue resulting in injury severe enough for tissue damage (e.g., alcohol abuse, minor nail biting, or binge eating), we reclassified them as “no, none of these.”
In order to analyze frequency of SIB, we asked: “On average, how often in the past year did you hurt yourself on purpose, without intending to kill yourself?” The response categories included: (1) Once or twice, (2) Once a month or less, (3) 2 or 3 times a month, (4) Once or twice a week, (5) 3 to 5 days a week, or (6) Nearly everyday, or everyday.
The primary SIB variable used in statistical analyses was whether the participant engaged in any form of SIB over the past year, although some analyses focused on specific types of SIB or the total number (range = 0–12) of types of SIB in which participants engaged. We focused on any SIB as the primary outcome because research in this area is in its infancy, and, as such, data on variations between specific types of SIB or the meaning of multiple forms of SIB is lacking.
Substance use behaviors
We asked about the frequency of binge drinking (past 2 weeks) and cigarette smoking (past 30 days). Questions about smoking (“On average, how many cigarettes did you smoke in the past 30 days?”) and binge drinking (“Over the past 2 weeks, on how many occasions did you have [5 if male, 4 if female] drinks in a row?”) were taken from the College Student Life Survey (Boyd & McCabe, 2007) and the College Alcohol Study (Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994), respectively. Questions about marijuana use, cocaine use, heroin use, methamphetamine use, ecstasy use, and other drug use without prescription all asked simply about any use within the past 30 days.
Because certain substance use variables were positively skewed, we created binary versions of each variable. For example, on the binge drinking item, 57% of respondents said they had not engaged in any binge drinking in the past 2 weeks, 14.6% said they had 1 episode of binge drinking, 12.4% had 2 episodes, 12.6% had 3 to 5 episodes, 2.8% 6 to 9 episodes, and 0.5% admitted to 10 or more episodes. Because it is difficult to interpret the clinical significance of the difference between 3 to 5 versus 6 or more episodes, we felt it would be easier to interpret the binge drinking data with the binary version of this variable (i.e., none versus any). In addition, we created a binary variable for “frequent binge drinking,” defined as at least 3 binge drinking occasions in the past 2 weeks (McCabe, 2002; Presley & Pimentel, 2006; Wechsler et al., 2002) and cigarette smoking (any cigarette smoking within the past 30 days).
Other risk behaviors
Gambling was assessed by asking “In the past 12 months, on approximately how many days did you make any sort of bet? (By “bet” we mean betting on sports, playing cards for money, playing gambling games online, buying lottery tickets, playing pool for money, playing slot machines, betting on horse races, or any other kind of betting or gambling).” Responses were indicated by frequency of gambling occurrence. Binge eating was assessed by asking a question adapted from the Structured Clinical Interview for DSM-IV (SCID): “Do you have eating binges in which you eat a large amount of food in a short period of time and feel that your eating is out of control?” Responses were indicated based on frequency of episodes per week.
Depression
Symptoms of depression in the past 2 weeks were measured using the Patient Health Questionnaire-9 (PHQ-9), a screening instrument based on the nine DSM-IV criteria for a major depressive episode (Spitzer, Kroenke, & Williams, 1999). Sample items include “Over the past 2 weeks, how often have you been bothered by feeling down, depressed, or hopeless?” “Over the past 2 weeks, how often have you been bothered by little interest or pleasure in doing things?” Response options are not at all, several days, more than half the days, and nearly every day. We used the PHQ-9 to determine the participant’s raw score and used this continuous variable in the analyses. The PHQ measures of mental health symptoms have been validated in a variety of populations (e.g., Diez-Quevedo, Rangil, Sanchez-Planell, Kroenke, & Spitzer, 2001; Henkel et al, 2004; Kroenke, Spitzer, & Williams, 2001; Lowe et al, 2004). For example, Spitzer et al. assessed the sensitivity and specificity of the PHQ in a sample of 585 family medicine and general internal medicine patients. Within 48 hours of completing the PHQ, patients were interviewed and diagnosed by a mental health professional (a clinical psychologist or psychiatric social worker). Evaluated against the clinical diagnoses, the PHQ had a sensitivity of .73 and specificity of .98 for major depressive disorder.
Sociodemographic characteristics
We collected information on the following socio-demographic characteristics: gender, academic level (graduate or undergraduate status), age, race/ethnicity, nationality (United States or international), living arrangement, sexual orientation, current financial situation, financial situation when growing up, and current relationship status.
Statistical Analysis
Using χ2 analyses, we first estimated the prevalence of SIB overall, by academic level, and by university. Second, we examined the prevalence of SIB by gender, race, and sexual orientation. We then investigated the different types of SIB by academic level and substance use.
Next, we estimated univariate logistic regression models predicting SIB and candidate predictor variables including: sexual orientation, student status, binge drinking, drug use, smoking cigarettes, binge eating, gambling, and depression.
Finally, we tested a multivariate logistic regression model predicting SIB as a function of candidate predictor variables. All analyses incorporated the nonresponse adjustment weights described above and were performed using SPSS 16.0.
ResultsA total of 5,689 (43.7%) students completed the main survey. The overall response rate (44%) was similar to other large-scale studies that have been reported (Cook, Heath, & Thompson, 2000; McCabe et al., 2007; Reifman, Watson, & McCourt, 2006; Wechsler et al., 2002).
Response rates were higher among graduate students (53%) than undergraduates (42%) and among females (50%) than males (36%). The sample was comprised of 69.6% undergraduate and 30.4% graduate students. While graduates had higher response rates, there were more undergraduate students total, which is why undergraduates represented a higher percentage within the sample. Among undergraduate students, 61.9% were female, and the race/ethnicity breakdown was 72.3% White, 7.5% Asian, 4.2% African American, 6% Hispanic, and 9.8% were Multi-Racial or Other. Most undergraduate students (86.1%) were in the “18–22 years” age category. Among graduate students, 61.7% were female, and the race/ethnicity breakdown was 62.7% White, 17.8% Asian, 6% African American, 4.1% were Hispanic, and 9% were Multi-Racial or Other. Most graduate students (92.6%) were in the “23 years and older” age category.
Prevalence of SIBs
The overall prevalence of past one-year SIB was 14.3%. Nearly 16% (N = 595) of undergraduate and 10% (N = 176) of graduate students reported any self-injury over the last year, χ2(1) = 68.2, p < .05. As seen in Table 1, undergraduates engaged in all types of self-injury more frequently than graduates. Among those who reported any SIB, the average number of SIBs was 2.0 (SD = 1.5). Significant undergraduate and graduate differences were found in cutting oneself, burning oneself, scratching oneself, biting oneself, carving something into skin, rubbing sharp objects into skin, and punching an object in order to hurt oneself. The most common form of SIB was punching oneself for both undergraduates and graduates (33.6% and 33.0%, respectively). The least common form of SIB was carving something into one’s skin for undergraduates and rubbing sharp objects into one’s skin for graduates (5.7% and 0.3%, respectively). Frequency of SIB was also analyzed (See Table 1). Overall, 84.3% of self-injurers participated in SIB once a month or less and 15.7% of self-injurers participated in SIB two to three times a month or more.
Past-Year Prevalence (%) of Self-Injury Among Undergraduate and Graduate Students
Past-year SIB rates varied significantly across different educational levels, ranging from 8.2% for advanced (beyond fourth year) graduate students up to 17.9% for first year undergraduates χ2(9) = 74.7, p < .01. Past-year SIB rates also differed significantly across the 13 universities, ranging from 9.8% to 19.4%, χ2(12) = 74.9, p < .01. Notably, prevalence rates of SIB were higher at medium (15.2%) and larger (15.1%) versus smaller (11.6%) schools, χ2(2) = 22.7, p < .001. In addition, rates were higher at public (15.0%) versus private (12.1%) schools, χ2(1) = 15.1, p < .001. Prevalence rates did not significantly differ across schools based on U.S. News and World Report (2008) reputation score (on a scale of 0–5, where 5 is the best), with SIB rates of 14.6% for those below the median and 14.0% for those above the median.
There were no gender or race differences in rates of past-year self-injury. Those who identified as GLBT had higher rates of self-injury relative to the heterosexual group. Relative risk in presented in Table 2; actual percentages of any SIB within each category are 13.1% of those who identified as heterosexual, 34.8% of those who identified as bisexual, 23.7% of those who identified as gay/lesbian/queer, and 23.1% of those who identified as “other”, χ2(3) = 57.3, p < .001.
Univariate Logistic Regression Model Odds Ratios for Prediction of Self-Injurious Behavior (SIB) With Sexual Orientation, Student Status, Binge Drinking, Drug Use, Smoking Cigarettes, Binge Eating, Gambling, and Depression
Prevalence of SIBs by Academic Level and Substance Use
Rates of past-year SIB were significantly different for the three levels of substance use (none, 11.0%; binge drink only, 13.7%; drug use, 25.3%), χ2(2) = 257.96, p < .001. Drug use, including marijuana, was associated with higher rates of all forms of self-injury, whereas binge drinking alone was not. This difference was significant for the sample as a whole, and for undergraduates and graduates when analyzed separately.
To address the question of whether frequency of binge drinking or drug use conferred greater risk for SIB, we conducted two logistic regression analyses predicting SIB by each of these variables (i.e., frequent binge drinker with non binge drinkers as the reference group, and drug using students with non drug using students as the reference group). Although frequent binge drinkers had increased risk of SIB (odds ratio [OR] = 1.7, p < .001), drug use was associated with nearly a three-fold risk of SIB (OR = 2.7, p < .001). Therefore, drug use was the focus of subsequent analyses.
Univariate Logistic Regression Analysis Predicting SIB as a Function of Candidate Predictor Variables
Table 2 presents logistic regression analyses predicting SIB with each of the candidate predictor variables covered in the literature review. Specifically, sexual orientation (OR = 1.4), student status (OR = 1.7), binge drinking (OR = 1.5), drug use (OR = 2.5), smoking cigarettes (OR = 2.3), binge eating (OR = 1.7), gambling (OR = 1.3), and depression (OR = 1.1) are all significant predictors of any SIB (p < .01).
Multiple Logistic Regression Analysis Predicting SIB as a Function of Candidate Predictor Variables
Variables with significant univariate relationships with SIB were simultaneously entered into a multiple logistic regression model (Table 3). While all predictors had significant univariate relationships with self-injury (i.e., Table 2), our final logistic regression model indicated that depression (OR = 1.14), cigarette smoking (OR = 1.45), gambling (OR = 1.28), and drug use (OR = 1.75) were significant independent predictors (at p < .05) of past-year self-injury, but binge drinking and binge eating were not. Note that results from a follow-up analysis using each predictor in its full (nonbinary) range yielded results that were highly comparable, but more difficult to interpret.
Final Logistic Regression Model Odds Ratios for Prediction of Self-Injurious Behavior (SIB) With Simultaneous Entry of Sexual Orientation, Student Status, Binge Drinking, Drug Use, Smoking Cigarettes, Binge Eating, Gambling, and Depression
DiscussionThis study addressed two questions: (1) What is the prevalence of self-injury among college students, overall and by gender, academic level (undergraduate vs. graduate status), and sexual orientation? (2) To what extent is self-injury associated with different forms of substance use (cigarette smoking, binge drinking, illicit drug use) and related risk behaviors (gambling, binge eating)?
With respect to our first question, we found no differences in SIB prevalence were observed for either gender or race. Our findings are consistent with previous work showing that the prevalence of SIB is similar across gender and racial groups (Gollust et al., 2008; Heath et al., 2008).
Furthermore, we found that overall, undergraduate students were significantly more likely than graduate students to engage in SIB. This finding replicates earlier studies where rates differed in this same direction (e.g. Gollust et al., 2008). However, more recently, Whitlock et al. (2008) found no differences between undergraduate and graduate prevalence rates. This finding could be in part to the latter study’s truncating the age of participants at 24. Our sample’s age range extended far beyond 24, possibly accounting for the divergent finding. That is, the effect of age on SIB could be because of people maturing out of this behavior. Alternatively, selection factors may be operating, whereby those who engage in this behavior are less likely to attend graduate school. Nonetheless, in our study, the prevalence reported by graduate students reported is still high.
Furthermore, this study replicated previous reports that students who identify as gay, lesbian, queer or transgender tend to have higher rates of SIB than their heterosexual peers (e.g. Deliberto & Nock, 2008; Gollust et al. 2008; Whitlock, Eckenrode, & Silverman, 2006). The higher rate of SIB in the Gay-Lesbian-Transgender-Queer (GLTQ) population, while replicated in many studies, does not have clear supporting evidence regarding why these rates are higher. Perhaps the rates could reflect more negative affect (King et al., 2008; Westefeld, Maples, Buford, & Taylor, 2001) the impact of social stigma and marginalization (DeLiberto & Nock, 2008; Scourfield, Roen, & McDermott, 2008), or conflicting feelings of sexual identity (Beckinsale, Martin, & Clark, 2001). More research is needed in this area to understand the mechanism driving this relationship.
With regard to our second question, those who reported drug use—including but not limited to marijuana use—had significantly higher rates of SIB relative to those who engaged in binge drinking in the absence of drug use or who denied both binge drinking and drug use. This difference was evident within both the graduate and undergraduate subsamples. Furthermore, drug use was associated with higher rates of all types of SIB, again both for undergraduates and graduates. Notably, the highest rate of SIB (62%) was observed among graduate students who reported both smoking cigarettes and using illicit drugs. Substance use is more prevalent in undergraduate students. However, students generally mature out of these behaviors by graduate school. As such, graduate students who continue to engage in substance use are an atypical group. Further it is possible that these graduate students are experiencing higher levels of distress which result in substance use and SIB. Therefore, their behavior could be more pathological in nature. This finding suggests that they might merit more intensive prevention efforts than are typically in place for such students.
Finally, in regard to whether demographic, substance use, and risk behaviors elevate risk of engaging in SIB, we found that in most cases, they do. Specifically, drug use, cigarette smoking, gambling, depression, sexual orientation and undergraduate student status were all associated with increased odds of engaging in SIB, both at the bivariate level and in combination, when collectively entered into a logistic regression model. As mentioned, the relationship between binge eating and SIB was mediated by depression.
Notably, past 2-week binge drinking behavior, per se, was not a significant predictor of SIB, but frequent binge drinking was. The lack of association between SIB and less frequent binge drinking is consistent with research suggesting that, overall, binge drinking among college students is not significantly associated with poor mental health. Dawson et al. suggested that this null association could be a selection effect, reasoning that those with co-occurring disorders are less likely to attend college. Another possibility is that binge drinking is considered a routine part of college life, and as such, it is not tied to affect regulation drinking motives (cf. Chassin, Pitts, & Prost, 2002). These theories have not been implicated in relation to SIB, but they might have the potential to advance our understanding of the relationship between SIB and binge drinking.
Specifically, our results suggest that campus prevention resources, which are typically quite limited, may be best directed at more frequent binge drinkers and those who use illicit drugs, rather than focusing on all college student drinkers.
Limitations
Our results should be interpreted in the context of several limitations to our study. A limitation is that assessment of SIB and psychological correlates were based on self-reports and online screening measures. Although these measures have good psychometric properties, it is not clear if the use of diagnostic interviews would yield similar results.
In addition, the cross-sectional design of our study precluded the identification of temporal order of associations between SIBs, demographic variables, substance use behaviors, and related risky behaviors. It is possible that the risk behaviors and SIB have overlapping initiation. Furthermore, it is plausible that there is significant individual variation in the initiation of these risky behaviors and SIB. Moreover, it is possible that the risk behaviors and SIB share common possible causal links, such as impulsivity. Future research may shed light on these relationships in a way that our cross-sectional survey—which did not assess timing of onset and offset of risk behaviors—does not permit.
The cross-sectional design also precluded examination of whether lower SIB among graduate students might be due to selection (e.g., undergraduates who engage in SIBs are less likely to pursue graduate education; cf. McCabe et al., 2007; Wood, Sher, & McGowan, 2000), socialization, effects of the academic context, age effects, or some combination of these processes. To our knowledge, the theory of “maturing out”, which is often discussed in the binge drinking literature, has not been explored in relation to Hawton and Harriss (2008) did identify that rates of SIB are higher in ages 20 to 34, compared with ages 10 to 19 and 35 to 59, however, this study was completed in a clinical population who presented with suicidal intent. Other researchers have conjectured that SIB peaks during mid-adolescence and declines into adulthood (Jacobson & Gould, 2007). It is possible that SIB follows the same trend as binge drinking, but this is still unclear.
In addition, our binge drinking measure did not account for quantity of alcohol consumed per occasion, beyond the 5/4 cut-off. The most recent binge-drinking literature suggests that a better indicator for problematic alcohol use is defined by quantity of alcohol consumed per occasion. As such, it may not be the frequency of binge drinking that is the most problematic, but the amount of alcohol consumed (Jackson & Sher, 2008). Thus, in future research it would be useful to include items assessing the quantity of alcohol consumed per occasion and more clearly specifying the duration of “an occasion.”
Furthermore, as self-injury increases in prevalence, people have also become more creative in their methods of self-injury. As recently reported in the popular press, hospitals are now seeing a type of self-injury called “self-embedding” where adolescents embed glass, paper clips and other objects into their skin (Peck, 2008). This type of self-injury requires a surgical procedure to remove the item. As SIBs morph, assessment tools likewise must account for these new behaviors. This study assessed many SIBs but perhaps missed other newer types of SIB.
Moreover, assessment of SIB frequency varies across studies. This study quantified frequency of SIB in units of time such as weekly or monthly over the past year. In other studies the frequency of SIB was calculated based on number of times over the lifespan. For example, Heath et al. (2008) assessed frequency of SIB as: once, two to four times, five to ten times, 11 to 50 times, and 100 or more times over the lifespan. Gratz et al. (2002) measured lifetime frequency using a different metric, i.e., more than 10 times in the past and more than 100 times in the past. Without a standardized way to measure frequency of SIB it is difficult to compare rates across populations and studies.
Strengths
Despite these limitations, our study has several methodological strengths. To our knowledge, no studies have examined how the co-occurrence of self-injury, substance use, and other related behaviors varies by academic level. Additionally, data for this study were collected from thousands of students across 13 U.S. universities, and, as such, is one of the first studies of this magnitude to look at SIB among college students.
Examination of subgroup differences, using a sample of this magnitude, may inform development of targeted prevention efforts aimed at reducing self-injury, substance use, and their co-occurrence. Furthermore, the use of a probability sample increases confidence in the generalizability of our findings to this population, and we used response propensity weights to adjust for nonresponse bias. The relatively large sample size allowed us to test gender and academic level as potential moderators of the associations between self-injury and substance use behaviors, and we statistically controlled for several demographic variables and risk factors.
Also, despite its limitations, our study makes several important substantive contributions. First, our results indicate that associations between self-injury and substance use in college samples vary by drug use and academic level. Second, we found that candidate predictors of SIB include sexual orientation, student status, drug use (including marijuana), smoking cigarettes, binge eating, gambling, and depression. Furthermore, our study indicated that binge eating, after analysis controlling for depression, was no longer a predictor of SIB. To our knowledge, this is the first study to report this pattern of associations.
ConclusionsTaken together, results from the current study lend strong support to the heightened risks associated with SIB. This study highlights the importance of distinguishing the effects of academic level, substance use, smoking, binge eating, gambling, and depression on SIB. As such, our findings may allow for more focused prevention and intervention efforts that target subgroups of students at greater risk for particular patterns of co-occurrence.
This is one of the first studies of this magnitude to look at the association between substance use and self-injury in a college student population. Perhaps most notably, the relationship between drug use and well established comorbidities (smoking, binge eating, depression, gambling) are significantly stronger amongst those who self-injure. The heightened association between drug use and self-injury in combination could increase lethality.
Although current prevention and intervention programs focused on binge drinking on college campuses exist, this study provides additional details that will allow campus administrators to target of substance users who are at high risk of self-injury. Instead of a blanket prevention program targeting all binge drinkers, perhaps universities should target those students who are participating in frequent binge drinking and drug use. Capturing these students and screening proactively for SIB and additional risky behaviors could aid in the intervention and treatment before these symptoms are exacerbated.
Results of the present study can inform college campus efforts to establish empirically grounded policy and prevention efforts to reduce SIB among their students. Study findings shed light on the risk factors for college student self-injury and may ultimately inform the design of prevention and intervention efforts. Such information is critical as colleges continue to grapple with risky behaviors such as self-injury and substance use. Finally, because of the gravity of self-injury and the associated risky behaviors it is necessary for clinicians and counseling centers to be appropriately prepared to encounter students manifesting with these issues. Research on the treatment of SIB is needed in nonclinical and subclinical populations to advance our understanding of the most effective ways to combat this behavior and prevent associated deleterious consequences.
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Submitted: March 5, 2009 Revised: July 13, 2009 Accepted: July 14, 2009
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Source: Psychology of Addictive Behaviors. Vol. 24. (1), Mar, 2010 pp. 119-128)
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Record: 54- Social connections and suicidal thoughts and behavior. You, Sungeun; Van Orden, Kimberly A.; Conner, Kenneth R.; Psychology of Addictive Behaviors, Vol 25(1), Mar, 2011 pp. 180-184. Publisher: American Psychological Association; [Journal Article] Abstract: Disrupted social connectedness is associated with suicidal thoughts and behaviors among individuals with substance use disorders (SUDs). The current study sought to further characterize this relationship by examining several indices of social connectedness—(a) living alone, (b) perceived social support, (c) interpersonal conflict, and (d) belongingness. Participants (n = 814) were recruited from 4 residential substance-use treatment programs and completed self-report measures of social connectedness as well as whether they had ever thought about or attempted suicide. Multivariate results indicated that interpersonal conflict and belongingness were significant predictors of a history of suicidal ideation, and that belongingness, perceived social support, and living alone were significant predictors of suicide attempt. These results indicate the most consistent support for the relationship between suicidality and thwarted belongingness, and also support the clinical utility of assessing whether individuals live alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Social Connections and Suicidal Thoughts and Behavior
By: Sungeun You
Chungbuk National University, Cheongju City, South Korea
Kimberly A. Van Orden
Department of Psychiatry, University of Rochester Medical Center, Rochester, New York;
Kenneth R. Conner
University of Rochester Medical Center and Canandaigua VA Center of Excellence, Rochester, New York
Acknowledgement: This research was supported, in part, by a grant from the National Institute on Alcohol Abuse and Alcoholism to Kenneth R. Conner (5R01AA016149-04) and by a grant from the National Institute of Mental Health to Yeates Conwell (5T32MH020061-09).
Substance dependence confers elevated risk for suicidal ideation (i.e., thoughts about killing oneself; Grant & Hasin, 1999) and suicide attempts (i.e., attempting suicide but surviving; Kessler, Kendler, Heath, Neale, & Eaves, 1992), as well as suicide (i.e., suicide attempts that result in death; Wilcox, Conner, & Caine, 2004). Reviews of the suicide literature have estimated that the risk for suicide among individuals with SUDs is five times greater or more than is that of the general population (Wilcox et al., 2004; Yoshimasu, Kiyohara, & Miyashita, 2008). The identification of psychological and social processes that elevate risk for suicidal thoughts and behaviors (i.e., ideation, attempts, or death) among individuals with substance use disorders (SUDs) represents one avenue for increased understanding of etiological mechanisms, as well as improved prevention efforts.
Several theories of suicide posit a central role for social connectedness in the etiology of suicide. Durkheim's sociological model proposes that too little social integration is one of several dysregulated social forces that causes suicide (Durkheim, 1897), and Shneidman's cubic model of suicide (Shneidman, 1987) proposes that an unmet need for “affiliation” is one of several needs that contribute to suicide when unmet. The interpersonal theory of suicide (Joiner, 2005; Van Orden et al., in press) proposes that the need to belong to caring and supportive relationships (Baumeister & Leary, 1995) is so powerful that, when thwarted, contributes to a desire for suicide. Several studies specifically examining the relationship between belongingness—the degree to which individuals perceive that they are meaningfully connected to satisfying (and positive) relationships or social groups—and suicidal desire have supported the theory (Conner, Britton, Sworts, & Joiner, 2007; Joiner, Hollar, & Van Orden, 2006; Joiner et al., 2009; Van Orden, Witte, Gordon, Bender, & Joiner, 2008; Van Orden, Witte, James, et al., 2008), although only one report used a substance-dependent sample (Conner et al., 2007).
Empirically, indices of social connectedness are related to suicidal thoughts and behavior among individuals with SUDs in several ways. First, living alone is associated with suicide (Murphy, Wetzel, Robins, & McEvoy, 1992) and suicide attempts (Haw, Houston, Townsend, & Hawton, 2001). Second, low social support is associated with suicide attempts (Darke et al., 2007; Johnsson & Fridell, 1997; Kingree, Thompson, & Kaslow, 1999). Third, perceptions of belongingness are also related to a lower likelihood of a past suicide attempt (Conner et al., 2007).
Given the high degree of interpersonal impairment associated with substance dependence (Segrin, 2001), interpersonal factors may be especially important targets for suicide prevention in substance-dependent populations. Indeed, psychological autopsy studies (though uncontrolled) indicate that partner–family relational discord is more common among SUD individuals who died by suicide than among those with mood or anxiety disorders (Duberstein, Conwell, & Caine, 1993; Heikkinen et al., 1994; Rich, Fowler, Fogarty, & Young, 1988). Given the high prevalence of interpersonal problems among individuals with SUDs who die by suicide, the aim of the current study is to examine the relationships between suicidal ideation and attempts with several social connectedness indices simultaneously in order to identify which measure (or measures) of social connectedness may be especially relevant to suicidality among individuals with SUDs. With few exceptions (Conner et al., 2007; Duberstein et al., 1993; Heikkinen et al., 1994), studies of suicidal thoughts and behavior have used only a single measure of social connectedness, precluding comparisons among measures.
We used indices of social connectedness across several levels of analysis as proposed by Berkman, Glass, Brissette, and Seeman (2000). At the first level, structural components of the social network were measured by whether participants lived alone; perceived social support and perceived degree of conflict in relationships were measured at an intermediate level; and at the most microlevel, the inner state of thwarted belongingness was measured, which is presumed to reflect an unmet need to belong to meaningful relationships (Baumeister & Leary, 1995). We hypothesized that all indices of social connectedness would be associated with suicidal behaviors such that greater degrees of connection would be associated with reduced probability of a past suicide attempt and suicidal ideation.
Method Procedure
Participants were recruited from four residential substance-use treatment programs in upstate New York. Following brief announcements, participants who were interested in study participation were scheduled for a one-on-one screening session lasting about 30 min. All participants completed self-report questionnaires and received a $10 gift card. A small proportion of participants went on to complete a more in-depth research battery; the present results focus only on the screening data. The study procedures were approved by the institutional review board of the University of Rochester Medical Center and the University of Buffalo.
Participants
A total of 814 patients participated in the study. There were 584 men and 228 women, and 2 participants did not report their gender. The mean age of participants was 39.0 years (SD = 11.3), and 219 (26.9%) reported having fewer than 12 years of education. Of the sample, 477 (58.6%) identified themselves as non-Hispanic White, 282 (34.6%) as non-Hispanic Black, and 55 (6.8%) as other race/ethnicity. Diagnostic data are not available for the sample, because these data were collected as part of a brief screen.
Measures
Outcomes: suicidal ideation and suicide attempt
Lifetime attempt was assessed using a question (“Have you ever tried to kill yourself or attempt suicide?”) that shows high test–retest reliability (91.8% agreement, κ = .82) in substance-dependent patients (Conner et al., 2007). Lifetime ideation was assessed using a question from the National Comorbidity Survey (“Have you ever seriously thought about committing suicide?”; Kessler, Borges, & Walters, 1999). Three mutually exclusive groups included history of suicide attempt, with or without suicidal ideation (N = 207, 25.4%), no history of suicide attempt but history of suicidal ideation (N = 168, 20.6%), and no history of ideation or attempts (N = 439, 53.9%).
For the secondary analyses of attempters, two mutually exclusive subgroups were created using an item from the National Comorbidity Survey with the procedure described by Nock and Kessler (2006) to discriminate suicidal gestures without intent to die (“My attempt was a cry for help, I did not intend to die”) versus suicide attempts with intent (“I tried to kill myself, but knew the method was not foolproof” or “I made a serious attempt to kill myself and it was only luck that I did not succeed”). An item created for the project asking, “How did you feel after the attempt?” was used to create two mutually exclusive subgroups of those happy to be alive after the attempt (“100% wanted to be alive” or “Mostly wanted to be alive”) versus those who regretted surviving (“Mostly wanted to be dead” or “100% wanted to be dead”).
Assessments of social connectedness
The Interpersonal Needs Questionnaire (Van Orden, Witte, Gordon, et al., 2008) was used to assess belongingness with higher scores indicating more belongingness (internal consistency, α = .81). Participants were asked to rate 10 questions assessing one's beliefs about the degree to which they feel they belong to others on a 7-point Likert scale from not at all true for me to very true for me (α = .81). An example item is “These days I am close to other people.” Perceived social support was assessed with the Kessler Perceived Social Support scale (KPSS; Kessler et al., 1992), with higher scores indicating more social support. The scale asks (a) “How much do the following people listen to you if you need to talk about your worries or problems?”, (b) “How much do the following people understand the way you feel and think about things?”, and (c) “How much do the following people go out of their way to help you if you really needed it?” Participants rate each question for five different social relationships (spouse, family, friends, religious groups, and neighborhood) on a 4-point Likert scale from not at all to a great deal, and rate the overall satisfaction on a 6-point Likert scale from very dissatisfied to very satisfied (“Overall, how satisfied are you with that?”). The sum of all items was used as the overall level of perceived social support (internal consistency, α = .93). Interpersonal conflict was measured with the Test of Negative Social Exchange (TENSE; Ruehlman & Karoly, 1991), with higher scores indicating more frequent negative social exchanges including hostility, insensitivity, interference, and ridicule. Participants were asked to rate how often they have experienced such behaviors in the past 3 months on a 5-point scale from not at all to about everyday (α = .93). To measure living status, participants were asked to report their usual living arrangements during the 90 days prior to inpatient admission. We formed three mutually exclusive groups: (a) living alone; (b) living with family (with partner/significant other, with partner and children, with children, with other family); and (c) other living arrangements (incarcerated/jail/prison, homeless, psychiatric unit, inpatient alcohol/drug treatment, and other). Of the sample, 23.8% (N = 190) reported living alone, 55.2% (N = 440) living with family, and 21.0% (N = 167) other living arrangements.
Assessments of covariates
Demographic covariates included age, gender, ethnicity (non-Hispanic White, non-Hispanic Black, and other race/ethnicity), and education (<12 years or ≥12 years). For primary substance use, participants were asked to answer the question of “Which drug, including alcohol, is your primary substance of use?” We formed three mutually exclusive groups on the basis of the primary substance: alcohol, cocaine, and other. In support of validity, the item was highly correlated with items asking which drug caused “the most difficulty” (r = .90, p = .01) and the drug that was used “most often” in the past year (r = .92, p = .01). For breadth of drug use, the numbers of drugs that were used more than 1–2 times per week were calculated to create a continuous variable of the breadth of drug use (Conner, Swogger, & Houston, 2009). Alcohol-related severity is assessed using the Alcohol Use Disorders Identification Test (AUDIT; Bohn, Babor, & Kranzler, 1995), a 10-item self-report measure of drinking and alcohol-related problems in the past year (α = .92) Although more often used as a screen, the AUDIT has also been validated for use in clinical substance-use populations as a continuous measure of alcohol-related severity (Donovan, Kivlahan, Doyle, Longabaugh, & Greenfield, 2006). The Physicians Health Questionnaire (PHQ; Spitzer, Kroenke, & Williams, 1999) was used to assess the severity of depressive symptoms, excluding the suicide item (α = .87).
Data Analytic Strategy
Using multinominal logistic regression models (Hosmer & Lemeshow, 2000), we compared three mutually exclusive, unordered groups of attempt, ideation, and nonsuicidal participants. The method of profile likelihood (McCullagh & Nelder, 1989) was used to compute odds ratios and 95% confidence intervals. We first conducted univariate tests for each predictor variable and covariates to compare the ideation and attempt groups with the nonsuicidal reference group. Predictors were perceived social support, belongingness, interpersonal conflict, and living alone. Covariates included gender (female, reference), age, ethnicity (White, reference), education (≥12 years, reference), primary substance use (alcohol, reference), breadth of drug use, alcohol-related problem severity, and depressive symptoms. In multivariate analyses, we simultaneously examined the relationships between indices of social connectedness at different levels of analysis and the outcomes of both suicidal ideation and suicide attempts. Variables that were not significantly associated with either ideation or attempt with p > .05 in a univariate test were removed from the subsequent multivariate test. Finally, in secondary analyses of individuals who had made a suicide attempt, we compared subgroups of attempters with low versus high intent to die, as well as subgroups who were glad to have survived versus wished they had died, on the indices of social connectedness. These analyses explore the extent to which the connectedness variables may differ as a function of these clinically relevant aspects of attempts. If connectedness is more strongly associated with more severe attempts (i.e., suicide intent) and with a continued longing for death (i.e., wished had died), then it would suggest the importance of a focus on connectedness in the prevention of more serious acts of suicide.
ResultsThe majority of the sample was male (n = 584, 71.74%) and the average age was 39.0 years (SD = 11.3). Most identified as non-Hispanic White (n = 477, 58.6%) or non-Hispanic Black (n = 282, 34.6%). The majority of the sample reported at least 12 years of education (n = 595, 73.1%). Most reported living with family (n = 451, 55.4%). As is seen in Table 1, concerning the covariates, univariate results (odds ratios, 95% confidence intervals, and p values, respectively) indicate that men were significantly less likely to report a past attempt (0.40, 0.28–0.56, p < .01) and those with less than 12 years of education were significantly more likely to report both ideation (1.50, 1.01–2.23, p < .05) and attempt (1.57, 1.09–2.26, p < .05). Neither age nor ethnicity was predictive of ideation or attempt. Both severity of alcohol-related problems and depressive symptoms were significantly related to ideation (AUDIT score: 1.02, 1.01–1.04, p < .05; PHQ-9 score: 1.09, 1.06–1.12 p < .05) and attempt (AUDIT score: 1.03, 1.02–1.05, p < .05; PHQ-9 score: 1.09, 1.06–1.12 p < .05).
Univariate and Multivariate Results of Multinominal Regression Models Predicting Lifetime Suicide Ideation and Attempt
Concerning the predictors of interest, as is seen in Table 1, univariate results show that decreased levels of perceived social support (0.98, 0.97–0.99, p < .01) and belongingness (0.96, 0.95–0.98, p < .01) were associated with greater probability of ideation. Likewise, decreased levels of perceived social support (0.98, 0.96–0.98, p < .01) and belongingness (0.97, 0.96–0.98, p < .01) were associated with greater probability of attempt. A 1-point decrease on the perceived social support measure increased the probability of having ideation by 2% (1%–3%) and attempt by 2% (1%–4%); a 1-point decrease on the belongingness measure increased the probability of having ideation by 4% (2%–5%) and attempt by 3% (2%–4%). Consistently, increased levels of interpersonal conflict were associated with greater probability of ideation (1.03, 1.02–1.05, p < .01) and attempt (1.02, 1.01–1.03, p < .01). Living alone was associated with greater probability of attempt (1.57, 1.04–2.35, p < .05) but was not associated with ideation at a statistically significant level. Finally, none of the social connectedness indices differentiated between subgroups of attempters with (a) low versus high intent to die or (b) low versus high regret over surviving, suggesting that the interpersonal variables are relevant to attempts broadly but may not distinguish a more severe subgroup of attempter.
Multivariate results are presented in Table 1. Three variables that were not associated with either ideation or attempt in univariate analyses (age, ethnicity, and primary substance of use) were removed from the multivariate analysis. After adjustment, lower levels of belongingness were associated with greater probability of both ideation (0.98, 0.96–1.00, p < .05) and attempt (0.98, 0.97–1.00, p < .05). Lower levels of perceived social support were associated with greater probability of attempt (0.98, 0.97–0.99, p < .01) but not with ideation at a statistically significant level. Individuals living alone were more likely to attempt suicide than were those living with family (1.74, 1.11–2.72, p < .05).
DiscussionThe current study examined the relationships among several indices of social connectedness and lifetime histories of suicidal ideation and suicide attempt among individuals in residential substance-use treatment programs. In line with predictions, all indices of social connectedness—interpersonal conflict, low perceived social support, low belongingness, and living alone—were associated with an increased probability of a history of suicide attempt and history of ideation (with the exception of living alone, which was associated with attempt only). In the multivariate model with all indices of social connectedness included, as well as covariates, interpersonal conflict and belongingness were significant predictors of a history of suicidal ideation, and belongingness, perceived social support, and living alone were significant predictors of suicide attempt. Thus, among individuals with SUDs, indices of current social connectedness at several levels of analyses are associated with lifetime histories of suicidal ideation and attempt. Future research could examine whether these indices may function as indicators of on-going elevated risk for suicidality. Finally, we found the most consistent support for the relationship between suicidal ideation and suicide attempts and belongingness, which is the form of social connectedness posited by the interpersonal theory of suicide to be a key factor in desire for suicide. Thus, our results provide additional empirical support for the theory and its applicability to patients treated for SUDs (Conner et al., 2007).
Our findings should be considered within the context of the study's limitations. Suicidal ideation and attempts were measured retrospectively, thus precluding an examination of temporal and causal relations. Furthermore, associations between social connectedness and current suicidality were not analyzed, and it is possible that some measures of social connectedness may display different relations with current suicidality. Other sources of heterogeneity of suicide attempts were not available; for example, data on the number of past attempts were not available, thus precluding an examination of whether indices of social connectedness function differently for multiple versus single attempters. We do not have diagnostic data for these participants, thus precluding analyses examining whether diagnostic categories function as either distal contributors to—or consequences of—social disconnection, thereby exploring one mechanism whereby mental disorders may elevate risk for suicide. Our sample consisted of adults receiving treatment at residential SUD treatment programs, thus caution must be taken when generalizing these findings beyond this high-risk population. An assessment of burdensomeness, the other key interpersonal predictor in the interpersonal theory, is not available.
Regarding clinical implications, the single-item question measuring whether or not participants lived alone is a quickly and easily administered index of social connectedness, and our data suggest that it is reliably associated with a history of a past attempt. Research is needed to investigate mechanisms whereby living alone confers risk; in the meantime, we suggest that clinicians working with SUD patients should routinely inquire about living status and take into consideration living alone in their suicidal behavior risk formulations. The measure of belongingness (Van Orden, Witte, Gordon, et al., 2008), a straightforward 10-item self-report scale, could also be administered and scored rapidly as part of a risk assessment. Future studies could investigate whether interventions for SUDs that specifically target patients' connectedness, particularly belongingness, reduce the risk for suicidal behavior.
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Submitted: November 11, 2009 Revised: July 1, 2010 Accepted: July 11, 2010
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Source: Psychology of Addictive Behaviors. Vol. 25. (1), Mar, 2011 pp. 180-184)
Accession Number: 2010-25604-001
Digital Object Identifier: 10.1037/a0020936
Record: 55- Social influences on smoking in middle-aged and older women. Holahan, Charles J.; North, Rebecca J.; Holahan, Carole K.; Hayes, Rashelle B.; Powers, Daniel A.; Ockene, Judith K.; Psychology of Addictive Behaviors, Vol 26(3), Sep, 2012 pp. 519-526. Publisher: American Psychological Association; [Journal Article] Abstract: The purpose of this study was to examine the role of 2 types of social influence—general social support and living with a smoker—on smoking behavior among middle-aged and older women in the Women's Health Initiative (WHI) Observational Study. Participants were postmenopausal women who reported smoking at some time in their lives (N = 37,027), who were an average age of 63.3 years at baseline. Analyses used multiple logistic regression and controlled for age, educational level, and ethnicity. In cross-sectional analyses, social support was associated with a lower likelihood and living with a smoker was associated with a higher likelihood of being a current smoker and, among smokers, of being a heavier smoker. Moreover, in prospective analyses among baseline smokers, social support predicted a higher likelihood and living with a smoker predicted a lower likelihood of smoking cessation 1-year later. Further, in prospective analyses among former smokers who were not smoking at baseline, social support predicted a lower likelihood and living with a smoker predicted a higher likelihood of smoking relapse 1-year later. Overall, the present results indicate that social influences are important correlates of smoking status, smoking level, smoking cessation, and smoking relapse among middle-aged and older women. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Social Influences on Smoking in Middle-Aged and Older Women
By: Charles J. Holahan
Department of Psychology, University of Texas at Austin;
Rebecca J. North
Department of Psychology, University of Texas at Austin
Carole K. Holahan
Department of Kinesiology and Health Education, University of Texas at Austin
Rashelle B. Hayes
Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School
Daniel A. Powers
Department of Sociology and Population Research Center, University of Texas at Austin
Judith K. Ockene
Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School
Acknowledgement: This work was supported by a grant from the National Institute on Drug Abuse (1R03DA025225-01A1). We acknowledge the contribution of all Women's Health Initiative Centers and their participants, staff, and investigators. In addition, we thank David Collins for assistance in preparing this article.
This article was prepared using a limited access dataset obtained from the National Heart Lung and Blood Institute (NHLBI) of the U.S. National Institutes of Health. The Women's Health Initiative (WHI) is conducted and supported by the NHLBI in collaboration with the WHI Study Investigators. The present manuscript was reviewed and approved for publication by the WHI Publications and Presentation Committee.
The U.S. Surgeon General has emphasized the importance of longitudinal research on smoking among women across adulthood (U.S. Department of Health and Human Services, 2001). Especially needed is an understanding of determinants of smoking in older women (Donze, Ruffieux, & Cornuz, 2007). A growing body of evidence indicates that social relationships shape health behavior throughout adulthood (Umberson, Crosnoe, & Reczek, 2010); however, research on social influences in smoking has focused primarily on adolescence. The purpose of this study was to examine the role of two types of social influence—social support and living with a smoker—in smoking among middle-aged and older women in the Women's Health Initiative (WHI) Observational Study.
Women and SmokingCigarette smoking is an important causal factor in morbidity and mortality among women in adulthood (Husten et al., 1997; LaCroix et al., 1991; Healthy People, 2010 (U.S. Department of Health and Human Services, 2000). From 2000 to 2004, almost 174,000 annual deaths among women were attributed to smoking-related causes, principally from cancer, cardiovascular diseases, and respiratory diseases (Centers for Disease Control and Prevention, 2008). Lung cancer now causes greater cancer-related mortality among women than breast cancer (U.S. Department of Health and Human Services, 2001). Cigarette smoking also contributes to many other cancers, as well as lower bone density and increased risk of hip fractures in postmenopausal women (U.S. Department of Health and Human Services, 2004).
Middle-aged and older women can reap substantial health benefits from smoking cessation (Burns, 2000; Hermanson, Omenn, Kronmal, & Gersh, 1988; Ockene, 1993; U.S. Department of Health and Human Services, 1990, 2001, 2004). Smoking cessation in middle-aged and older individuals is associated with improvement in immediate and longer-term health (Hermanson et al., 1988; Taylor, Hasselblad, Henley, Thun, & Sloan, 2002; U.S. Department of Health and Human Services, 1990). In addition, although fewer older adults attempt to quit smoking as compared with younger persons (U.S. Department of Health and Human Services, 2000), they are just as likely or more likely to be successful in their attempts to quit smoking (Burns, 2000; Sorlie & Kannel, 1990; Whitson, Heflin, & Burchett, 2006).
Social Support and SmokingSocial support has been a central focus in research on social ties and health behavior (Taylor & Repetti, 1997; Umberson et al., 2010). For example, the transtheoretical model of change lists helping relationships as a key process in changing health behaviors (Prochaska, Johnson, & Lee, 2009). However, although research evidence generally suggests a relationship between social support and smoking, effects of social support on smoking cessation have not been consistently significant (Lichtenstein, Glasgow, & Abrams, 1986). Further, while research has tended to focus on smoking-specific social support, general social support from family and friends may play an important role in smoking behavior (Wagner, Burg, & Sirois, 2004). Moreover, few studies have investigated social support and smoking in large samples of women (Väänänen, Kouvonen, Kivimäki, Pentti, & Vahtera, 2008), and fewer have investigated this relationship among middle-aged and older women.
Some cross-sectional evidence from community surveys documents an inverse association between social support and smoking status. Among over 20,000 mixed-aged female employees in Finland, low social support was positively associated with being a current smoker and heavier smoker and with a lower likelihood of reporting having quit smoking (Väänänen et al., 2008). Similarly, among almost 2,000 Lebanese women, those who experienced less trust and felt more isolated were more likely to smoke. However, these effects were observed only among younger women (Afifi, Nakkash, & Khawaja, 2010).
Additional prospective evidence documents a positive association between social support and smoking cessation in the context of smoking cessation interventions. For example, early research with community adults in a university-based smoking cessation program indicated that both perceived partner support and perceived general support predicted quitting smoking (Mermelstein, Cohen, Lichtenstein, Baer, & Kamarck, 1986). Further, a social support group intervention among employees from diverse worksites in the Chicago area predicted increased smoking cessation two years later (McMahon & Jason, 2000). Similarly, a social support addition to a behavioral smoking cessation intervention among residents of Calgary, Canada, improved cessation at three months among both women and men. However, at six months, cessation was maintained only among men (Carlson, Goodey, Bennett, Taenzer, & Koopmans 2002). Moreover, among lower-educated women in a community-based smoking cessation program, high social support weakened the negative relationship between history of depression and smoking cessation (Turner, Mermelstein, Hitsman, & Warnecke, 2008).
Effects of Living With a SmokerAlthough research on social ties and health behavior generally has assumed that social ties play a salutary role, in fact ties to others can also encourage health risk behaviors (Umberson, Crosnoe, & Reczek, 2010). In fact, for both adolescents and adults, negative health behaviors, such as smoking, often are learned and reinforced in group contexts (Taylor & Repetti, 1997; Väänänen et al., 2008). For example, after following over 9,000 middle-aged couples for up to eight years, Falba and Sindelar (2008) found that partners shape one another's health habits for good and for bad. Effects were especially strong for tobacco where a partner's behavior may operate as a smoking cue. Correspondingly, household smoking bans promote attempts to quit smoking and more successful cessation outcomes among community adults (Farkas, Gilpin, Distefan, & Pierce, 1999; Gilpin, White, Farkas, & Pierce, 1999). However, because most research in this area has focused on younger or mixed-age samples, little is known about the effect of living with a smoker among older women smokers.
Large-sample community surveys have shown consistent adverse effects for living with a smoker on smoking level and smoking cessation among younger Danish women (Mueller, Munk, Thomsen, Frederiksen, & Kjaer, 2007), recently married New York couples (Dollar, Homish, Kozlowski, & Leonard, 2009), and mixed-age British householders (Chandola, Head, & Bartley, 2004). Similarly, in the context of smoking cessation interventions, consistent adverse effects for living with a smoker on cessation have been observed in large samples of mixed-aged adults in Britain (Ferguson, Bauld, Chesterman, & Judge, 2005), Australia (Gourlay et al., 1994), and Italy (Senore et al., 1998). Moreover, there is some evidence from smoking cessation interventions with large mixed-aged samples of adults in the U.S. (Bjornson et al., 1995) and Australia (Moshammer & Neuberger, 2006) that women's smoking may be more adversely influenced than men's by living with a smoker.
In addition to its adverse direct effect on smoking, it is also plausible that living with a smoker may diminish the salutary effect of general social support on smoking outcomes. This question may be especially relevant to women, who are more likely to report inconsistency between perceived norms to quit smoking and the smoking behavior of a partner (Dohnke, Weiss-Gerlach, & Spies, 2011). Very little research has examined this question. Pollak and Mullen (1997) studied a small sample of pregnant women who had spontaneously quit smoking. General social support from a partner was positively associated with continued abstinence six weeks postpartum, but only for women whose partners did not smoke. Reflecting on this pattern of findings, the authors concluded that: “partners evidently cannot override their smoking with general social support” (p. 186). In a similar vein, alliance with a buddy to provide social support enhanced quitting in a self-help smoking cessation program in the Chicago area, but only when the buddy was not a continuing smoker (Kviz, Crittenden, Madura, & Warnecke, 1994).
The Present StudyThe present study examined the relation of two types of social influence—general social support in the emotional, informational, leisure, and tangible domains and living with a smoker—on smoking in 37,027 middle-aged and older women using data from the WHI Observational Study. The WHI Observational Study was framed to examine the role of lifestyle factors in the prevention of heart disease, some cancers, and osteoporosis in women who were postmenopausal (Hays et al., 2003). Participants were recruited from urban, suburban, and rural areas surrounding clinical centers in the United States. The WHI Observational Study presents a unique opportunity to examine the separate and interactive effects of social support and living with a smoker on a range of smoking outcomes in a large sample of middle-aged and older women. Smoking outcomes include point-prevalence measures of smoking status and smoking level at baseline and smoking cessation and smoking relapse assessed at a 1-year follow-up.
Two hypotheses were advanced. (a) Extending previous research on the role of social support in smoking in mixed-aged samples (McMahon & Jason, 2000; Väänänen et al., 2008), we hypothesized that social support would be negatively associated with smoking status, heavier smoking, and smoking relapse and positively associated with smoking cessation. (b) Extending previous research on the role of living with a smoker in smoking in mixed-aged samples (Chandola et al., 2004; Falba & Sindelar, 2008), we hypothesized that living with a smoker would be positively associated with smoking status, heavier smoking, and smoking relapse and negatively associated with smoking cessation. In addition, we examined one exploratory question. Extending previous research on the interactive roles of social support and living with a smoker on smoking among young women (Pollak & Mullen, 1997), we examined whether living with a smoker would weaken the hypothesized associations between social support and smoking outcomes.
Methods Sample Selection and Characteristics
The WHI Observational Study included women between the ages of 50 and 79 who were postmenopausal at enrollment between 1993 and 1998 (Hays et al., 2003). The original purpose of the WHI Observational Study was to explore the predictors and natural history of important causes of morbidity and mortality in postmenopausal women related to heart disease, cancers, and osteoporosis. Postmenopausal was defined as not having a menstrual period for at least 6 months if age was 55 years or older, and more conservatively as having no menstrual period for at least 12 months for younger women aged 50–54.
Inclusion criteria included the ability and willingness to provide written informed consent and plans to stay in the same area for at least 3 years. Potential participants were excluded if they had medical conditions that predicted survival of less than 3 years, or if they had conditions such as alcohol or drug dependency, mental illness, including severe depression or dementia, which might affect retention. The WHI sample was healthier and reported a lower prevalence of smoking than the general population of women in their cohort (Langer et al., 2003). The inclusion of participants from racial/minority groups proportionate to their age-group representation in the U.S. population was a priority (Hays et al., 2003). Details of the WHI design have been published previously (Hays et al., 2003; Langer et al., 2003; Women's Health Initiative Study Group, 1998). Of participants who completed the WHI screening form and who were not assigned to a clinical trial, 93,676 (30.9%) were successfully enrolled in the WHI Observational Study (Hays et al., 2003).
Because smoking initiation is unlikely in middle to later adulthood (Moon-Howard, 2003), the present analyses were restricted to participants who reported smoking at some time in their lives. Specifically, the present sample includes the 37,027 (40%) of baseline participants in the WHI Observational Study who reported that they “smoked at least 100 cigarettes” during their entire life and who also provided complete data on the measures used here. At baseline, the participants in the present sample were an average age of 63.3 (SD = 7.19) years. The majority of participants (59%) were married. The sample was predominantly White (86.6%), with the remainder of the sample American Indian/Alaskan Native (0.4%), Asian/Pacific Islander (1.5%), Black (8.0%), Hispanic (2.5%), and Unknown (1.0%). Both White and Black ethnicity categories specified not-of-Hispanic origin. In terms of education, 4.7% of participants had less than a high school education, 15.5% had a high school (or vocational school) education, 39.2% had some education beyond high school but had not completed college, and 40.5% had completed college.
Measures
Sociodemographic factors, social support, and living with a smoker were assessed at baseline and smoking outcomes were assessed at baseline and at a 1-year follow-up by self-report with standardized questionnaires.
Sociodemographic factors
Sociodemographic factors used as control variables included age (in years), educational level, and ethnicity. Educational level was operationalized as less than a high school (or vocational school) education, high school (or vocational school) education, some education beyond high school (or vocational school) but not having completed college, and completed college.
Social support
Social support was measured with 9 items from the Medical Outcomes Study Social Support Survey (Sherbourne & Stewart, 1991). The items tapped emotional, informational, leisure, and tangible dimensions of general support. Items were preceded by a prompt: “How often is each of the following kinds of support available to you if you need it?” A sample item is “Someone you can count to listen to you when you need to talk.” Responses ranged from 1, none of the time, to 5, all of the time. Total scores (range = 9–45) are the sum of scores for the nine items. In the present sample, Cronbach's alpha = .93.
Living with a smoker
Living with a smoker in one's home was indexed by a single item: “Does anyone living with you now smoke cigarettes inside your home?” (no = 0, yes = 1). Among participants living with a smoker, most (57.4%) household smokers were partners. Among participants living with a smoker who was not a partner, most household smokers (62.1%) were a daughter or son versus some “other person.”
Smoking outcomes
Four smoking outcomes were assessed. At baseline, we assessed smoking status and, among current smokers, smoking level. In addition, at a 1-year follow-up, we assessed smoking cessation among baseline smokers and smoking relapse among former smokers who were not smoking at baseline.
Smoking status at baseline was indexed based on responses to an item that asked “Do you smoke cigarettes now” (no = 0, yes = 1). Level of smoking among smokers was indexed based on responses to a question that asked, “On the average, how many cigarettes do you usually smoke each day?” Response choices were: Less than 1, 1–4, 5–14, 15–24, 25–34, 35–44, and 45 or more. Following Hatsukami et al. (2006) and Holahan et al. (in press), we operationalized light smoking as less than 15 cigarettes per day (score = 0) and heavier smoking as 15 or more cigarettes per day (score = 1). Among baseline smokers, smoking cessation at 1 year was operationalized as reporting no smoking (score = 1) versus smoking (score = 0) at the 1-year follow-up. Among former smokers who were nonsmokers at baseline, smoking relapse at 1 year was operationalized as reporting smoking (score = 1) versus no smoking (score = 0) at the 1-year follow-up.
Data Analysis Strategy
Multiple logistic regression analyses were used to analyze the relation of social influences to smoking outcomes. First, separate cross-sectional analyses examined smoking status and, among current smokers, smoking level at baseline. Next, separate prospective analyses examined smoking cessation among baseline smokers and smoking relapse among baseline nonsmokers at a 1-year follow-up. In each analysis, we began with a model that included social support and living with a smoker as predictors. Next, we examined a model that included the interaction between social support and living with a smoker. If the interaction was significant, it was retained in the model; if it was not significant, it was not retained in the model. All analyses controlled for age (in years), educational level (less than a high school education was the reference group), and ethnicity (White was the reference group).
Results Descriptive Statistics
At baseline, mean social support was 35.7 (SD = 7.9), and 4,012 participants (10.8%) lived with a smoker. Current smoking was reported by 4,834 participants (13.1%) at baseline. Baseline smokers began smoking at a median age of 15–19 years and had been regular smokers for a median of 30–39 years. Most baseline smokers (51.7%) were light smokers. Among baseline smokers, smoking cessation was reported by 706 of 4,407 participants (16.0%) who provided data at the 1-year follow-up. Among baseline nonsmokers, smoking relapse was reported by 349 of 30,516 participants (1.1%) who provided data at the 1-year follow-up.
Analyses of Missing Data and Attrition
Missing data
Overall, there were relatively little missing data, except for living with a smoker for which 15.4% of participants had missing data. Among the full sample of 45,304 baseline participants who reported smoking at some time in their lives, we compared participants who provided sufficient data to be included in the present analyses (N = 37,027) with those who did not provide sufficient data (n = 8,277, 18.3%). The only noteworthy difference involved ethnicity, χ2(5, N = 45,185) = 119.67, p > .01, with missing data most likely among Hispanics (27.5%) and least likely among Whites (17.4%).
1-year attrition
In addition, among the 37,027 participants included in the present analyses, we compared surviving participants (n = 34,923) with those who did not participate at the 1-year follow-up (n = 2,104, 5.7%). The only noteworthy differences involved educational level and ethnicity. For educational level, χ2(3, N = 37,027) = 283.03, p > .01, missing data were more likely among participants with less than a high school education (13.8%) compared with other educational groups (average of 5.3%). For ethnicity, χ2(5, N = 37,027) = 820.11, p > .01, missing data were most likely among American Indian/Alaskan Natives (14.6%), Blacks (15.7%), and Hispanics (14.5%), and least likely Whites (4.4%).
Baseline Smoking
Smoking status
We began by examining the cross-sectional association between social influences and current smoking status at baseline in a multiple logistic regression analysis (N = 37,027). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with current smoking status at baseline. Specifically, a 1 standard deviation increase in social support was linked to a 17% decrease in the odds of being a current smoker. Compared with not living with a smoker, living with a smoker was associated with a more than sixfold increase in the odds of being a current smoker.
Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was significantly positively related to current smoking status at baseline and was retained in the model. Specifically, living with a smoker attenuated the association between social support and smoking status. Results for the final model are presented in Table 1.
Results of Multiple Logistic Regression Analyses Predicting Current Smoking Status Among Participants Who Smoked at Some Time in Their Lives and, Among Current Smokers, Level of Smoking at Baseline
To illustrate the interactive effect of social support and living with a smoker, we examined the association between social support and smoking status under contrasting levels of living with a smoker. Among individuals who did not live with a smoker, each 1 standard deviation increase in social support was linked to a 20% decrease in the odds of being a current smoker. In contrast, among individuals who lived with a smoker, each 1 standard deviation increase in social support was linked to an 8% decrease in the odds of being a current smoker.
Smoking level
In addition, we examined the cross-sectional association between social influences and current smoking level among current smokers at baseline in a multiple logistic regression analysis (n = 4,834). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with current smoking level at baseline. Specifically, a 1 standard deviation increase in social support was linked to a 12% decrease in the odds of being a heavy smoker. Compared with not living with a smoker, living with a smoker was associated with a 34% increase in the odds of being a heavy smoker.
Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was significantly positively related to smoking level at baseline and was retained in the model. Specifically, living with a smoker attenuated the association between social support and smoking level among current smokers. Results for the final model are presented in Table 1.
To illustrate the interactive effect of social support and living with a smoker, we examined the association between social support and smoking level under contrasting levels of living with a smoker. Among individuals who did not live with a smoker, each 1 standard deviation increase in social support was linked to a 16% decrease in the odds of being a heavy smoker. In contrast, among individuals who lived with a smoker, each 1 standard deviation increase in social support was linked to a 4% decrease in the odds of being a heavy smoker.
Prospective Analyses
Smoking cessation
Next, we examined the prospective association between social influences and smoking cessation among baseline smokers in a multiple logistic regression analysis (n = 4,407). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with smoking cessation among baseline smokers. Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was not significantly related to smoking cessation and was not retained in the model. Results are presented in Table 2. Each 1 standard deviation increase in social support was linked to a 20% increase in the odds of quitting smoking. Compared with not living with a smoker, living with a smoker was linked to a 26% decrease in the odds of quitting smoking.
Results of Prospective Multiple Logistic Regression Analyses Predicting Smoking Cessation Among Baseline Smokers and Smoking Relapse Among Baseline Nonsmokers at 1-Year Follow-Up
Smoking relapse
We also examined the prospective association between social influences and smoking relapse among former smokers who were not smoking at baseline in a multiple logistic regression analysis (n = 30,516). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with smoking relapse among former smokers who were not smoking at baseline. Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was not significantly related to smoking relapse and was not retained in the model. Results are presented in Table 2. Each 1 standard deviation increase in social support was linked to a 20% decrease in the odds of relapsing into smoking. Compared with not living with a smoker, living with a smoker was associated with a 128% increase in the odds of relapsing into smoking.
DiscussionThe present findings demonstrate a consistent link between social influences and negative smoking-related behaviors among middle-aged and older women in the WHI Observational Study who smoked at some point in their lives. Extending previous research on the role of social support in smoking in mixed-aged samples (McMahon & Jason, 2000; Väänänen et al., 2008), we found that social support was consistently inversely associated with all of the smoking outcomes we investigated. Further, extending previous research on the role of living with a smoker in smoking in mixed-aged samples (Chandola et al., 2004; Falba & Sindelar, 2008), we found that living with a smoker was consistently positively associated with all of the smoking outcomes we investigated. The strength of these findings may be due in part to the nature of the sample. Current smokers in the sample were more likely to be light smokers, and light compared with heavier smoking is more likely to be influenced by environmental factors (Shiffman, 2009).
Specifically, general social support was associated with a lower likelihood and living with a smoker was associated with a higher likelihood of being a current smoker and, among smokers, of being a heavier smoker. Moreover, among baseline smokers, social support predicted a higher likelihood and living with a smoker predicted a lower likelihood of smoking cessation 1-year later. Further, among former smokers who were not smoking at baseline, social support predicted a lower likelihood and living with a smoker predicted a higher likelihood of smoking relapse 1-year later. All of these effects were unique contributions for both social support and living with a smoker controlling for one another as well as for age, educational level, and ethnicity.
General social support from family and friends may reduce smoking in several ways. A perception of positive regard from significant others may motivate self-care behaviors (Wagner et al., 2004). In addition, family and friends may explicitly endorse behaviors that enhance the health of loved ones (Väänänen et al., 2008). Further, social support may reduce stress and depressed mood (Umberson et al., 2010; Väänänen et al., 2008). Further, supportive others may further perceptions of self-efficacy toward desired health behaviors (Umberson et al., 2010).
On the other hand, living with a smoker may increase smoking in several ways. At a psychological level, for individuals attempting to quit smoking, abstinence from smoking on the part of partners or housemates may be perceived as a form of social support (Pollak & Mullen, 1997). Partners or housemates who smoke may also foster a household norm that legitimizes smoking behavior and signals a lack of communal commitment to reducing negative health behaviors more generally (Umberson, Crosnoe, & Reczek, 2010). In addition, at a behavioral level, smoking by others in the household provides smoking cues (Falba & Sindelar, 2008). More practically, living with a smoker also results in an easy availability of cigarettes (Chandola, Head, & Bartley, 2004).
Extending previous research on the interactive roles of social support and living with a smoker on smoking among young women (Kviz, Crittenden, Madura, & Warnecke, 1994; Pollak & Mullen, 1997), we found that living with a smoker weakened the cross-sectional inverse association of social support with both smoking status and smoking level. In contrast, the prospective association of social support with both smoking cessation and smoking relapse was independent of living with a smoker.
Social support may have been less reactive to living with a smoker in the context of change in health behavior where social support is especially likely to be sought and valued for promoting change (see Prochaska et al., 2009). Alternatively, social support may have been more stable than housemates' smoking behavior across the 1-year follow-up period. Whereas self-reports of available social support are highly stable across time (Sarason, Sarason, & Shearin, 1986), partners' health behaviors often change in unison (Falba & Sindelar, 2008) and household smokers may themselves have quit smoking at follow-up.
Some limitations should be kept in mind in interpreting these results. The WHI Observational Study measure of smoking relied on self-report. However, several comparisons of self-report with biochemical or cross-informant measures of smoking have found that self-report measures are accurate in most situations, particularly, as in the WHI, in studies of adults who are not in smoking intervention studies (Caraballo, Giovino, Pechacek, & Mowery, 2001; Rebagliato, 2002). Nevertheless, future research would be strengthened by including objective or collateral measures of smoking. Moreover, because participants in the WHI Observational Study were healthier and reported a lower prevalence of smoking than the general population of women in their cohort (Langer et al., 2003), the results may not generalize to all middle-aged and older women. Further, missing data on the variables examined here resulted in an underrepresentation of Hispanics in our baseline analyses and 1-year attrition resulted in an underrepresentation of several ethnic minority groups (Indian/Alaskan Natives, Blacks, and Hispanics), as well as participants with less than a high school education, in our follow-up analyses.
The present study has several strengths. A central contribution is the analysis of social influences and smoking in middle-aged and older women, a population that has been neglected in smoking research. Additional strengths are the large sample, the longitudinal design, and the availability of well-validated measures of social influences. Overall, the present results indicate that social influences are important correlates of smoking status, smoking level, smoking cessation, and smoking relapse among middle-aged and older women. Moreover, our findings demonstrate that the effects of social ties are complex. Whereas, positive social support discourages smoking, living with a smoker maintains it. In fact, as Umberson et al. (2010) have noted, the counter effects of positive and negative influences have likely resulted in an underappreciation of the role of social ties in health behavior.
Our results suggest that addressing social influences can contribute to the effectiveness of smoking intervention programs with middle-aged and older women. Our findings reinforce U.S. Public Health Service clinical practice guidelines for treating tobacco use (Fiore et al., 2000) that encourage incorporating social support both within and outside of treatment. At the same time, our findings underscore the need for a more textured appreciation of the adverse, as well as the salutary, effects of social ties. For example, training in cognitive-behavioral skills for relapse prevention might be tailored to include coping with the adverse effects of living with a smoker. Further, when partners or housemates smoke, group interventions, including household smoking bans, may be especially effective.
Footnotes 1 The WHI did not assess nondaily, intermittent smoking.
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Submitted: June 1, 2011 Revised: July 19, 2011 Accepted: August 16, 2011
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Source: Psychology of Addictive Behaviors. Vol. 26. (3), Sep, 2012 pp. 519-526)
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Record: 56- Sociodemographic and psychiatric diagnostic predictors of 3-year incidence of DSM–IV substance use disorders among men and women in the National Epidemiologic Survey on Alcohol and Related Conditions. Goldstein, Risë B.; Smith, Sharon M.; Dawson, Deborah A.; Grant, Bridget F.; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 924-932. Publisher: American Psychological Association; [Journal Article] Abstract: Incidence rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women, but information on whether sociodemographic and psychiatric diagnostic predictors of AUD and DUD incidence differ by sex is limited. Using data from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions, sex-specific 3-year incidence rates of AUDs and DUDs among United States adults were compared by sociodemographic variables and baseline psychiatric disorders. Sex-specific logistic regression models estimated odds ratios for prediction of incident AUDs and DUDs, adjusting for potentially confounding baseline sociodemographic and diagnostic variables. Few statistically significant sex differences in predictive relationships were identified and those observed were generally modest. Prospective research is needed to identify predictors of incident DSM-5 AUDs and DUDs and their underlying mechanisms, including whether there is sex specificity by developmental phase, in the role of additional comorbidity in etiology and course, and in outcomes of prevention and treatment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Sociodemographic and Psychiatric Diagnostic Predictors of 3-Year Incidence of DSM–IV Substance Use Disorders Among Men and Women in the National Epidemiologic Survey on Alcohol and Related Conditions / BRIEF REPORT
By: Risë B. Goldstein
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland;
Sharon M. Smith
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland
Deborah A. Dawson
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, and Kelly Government Services, Rockville, Maryland
Bridget F. Grant
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health
Acknowledgement: The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) is funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) with supplemental support from the National Institute on Drug Abuse. This research was supported in part by the Intramural Program of the National Institutes of Health, NIAAA. A preliminary version of parts of this article was presented at the 167th Annual Meeting of the American Psychiatric Association, May, 2014, New York, NY. The authors extend their thanks to S. Patricia Chou, PhD, and Tulshi D. Saha, PhD, for invaluable assistance with the revision of this article. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of sponsoring organizations, agencies, or the U.S. government.
Incidence (first onset) rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women across diagnostic systems, regardless of whether abuse and dependence are combined or considered separately (see Table 1). However, few studies have investigated whether predictors of incident AUDs and DUDs differ by sex. Bijl, de Graaf, Ravelli, Smit, and Vollebergh (2002) found no sex difference in prediction by age of incident AUDs in a nationally representative sample of adults in the Netherlands; Wittchen et al. (2008) reported similar findings from epidemiologically ascertained adolescents and young adults in the Munich Early Developmental Stages of Psychopathology (EDSP) cohort. Conversely, Mattisson, Bogren, Horstmann, and Öjesjö (2010) found a tendency toward later onsets of AUDs among women than men in the Lundby cohort.
Previous Incidence Studies Reporting Sex-Specific Rates of Alcohol and Drug Use Disorders
Zimmermann et al. (2003) did not find sex differences in prediction of incident AUD by anxiety disorders over a mean follow-up of 42 months among the Munich EDSP cohort. To our knowledge, however, the sex specificity of other predictors of AUD and DUD incidence, including unmarried status and existing psychiatric disorders (Crum, Chan, Chen, Storr, & Anthony, 2005; de Graaf, ten Have, Tuithof, & van Dorsselaer, 2013; Eaton et al., 1989; Grant et al., 2009; Newman & Bland, 1998; Zimmermann et al., 2003), has not been investigated. Both sociodemographic characteristics and existing psychiatric disorders may contribute differentially in men and women to the etiology and course of chronologically secondary AUDs and DUDs. Sex specificity could reflect differences in risk and protective factors, including gendered patterns of exposure to substances and social acceptability of their use. In addition to informing further etiologic investigations, identification of sex differences in predictors of AUD and DUD incidence may guide appropriate tailoring of preventive and therapeutic interventions for these and chronologically primary psychiatric disorders.
Accordingly, this study’s goals were to: (a) estimate sex-specific incidence rates of DSM–IV AUDs and DUDs in a large, nationally representative U.S. sample, (b) provide sex-specific data on sociodemographic risk factors, and (c) estimate sex-specific prediction of incident AUDs and DUDs by baseline Axis I and Axis II disorders.
Method Sample
The entire research protocol, including informed consent procedures, was approved by the institutional review board of the U.S. Census Bureau and the Office of Management and Budget. Wave 2 (W2) is the 3-year prospective follow-up of the Wave 1 (W1) National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) sample (Grant, Moore, Shepard, & Kaplan, 2003; Grant, Kaplan, Moore, & Kimball, 2007). The W1 NESARC (overall response rate = 81.0%, n = 43,093) represented U.S. residents ≥ 18 years old of households and selected group quarters. Individuals 18 to 24 years old, non-Hispanic Blacks, and Hispanics were oversampled. In-person reinterviews of all W1 respondents were attempted in W2. Among those alive, resident in the U.S., and not incapacitated or on active military duty throughout the follow-up period, the W2 response rate was 86.7% (n = 34,653); the cumulative response rate was 70.2% across the two waves. W2 respondents did not differ from W2 respondents plus eligible nonrespondents sociodemographically or on any W1 lifetime psychiatric disorder (Grant et al., 2009).
Assessments
Substance use disorders
Diagnostic assessments utilized the Alcohol Use Disorder and Associated Disabilities Interview Schedule—DSM–IV Versions (AUDADIS-IV) for Waves 1 (Grant, Dawson, & Hasin, 2001) and 2 (Grant, Dawson, & Hasin, 2004). DSM–IV criteria for alcohol and drug-specific abuse and dependence for 10 drug categories were queried at both waves (Compton, Thomas, Stinson, & Grant, 2007; Grant et al., 2009; Hasin, Stinson, Ogburn, & Grant, 2007). Abuse diagnoses required that ≥ 1 abuse criterion, and dependence diagnoses, ≥ 3 dependence criteria, be met in the same year for the same substance. Drug-specific disorders are aggregated to yield any drug abuse and any drug dependence. Nicotine dependence was diagnosed similarly (Grant, Hasin, Chou, Stinson, & Dawson, 2004). Reliability of AUDADIS-IV AUDs (κ = .70–.84), DUDs (κ = .53–.79), and nicotine dependence (κ = .60–.63), and their validity, are extensively documented in clinical and general population samples (Compton et al., 2007; Grant, Dawson et al., 2003; Hasin et al., 2007).
Other psychiatric disorders
DSM–IV primary mood (MDD, dysthymia, and bipolar I and II) and anxiety (panic, social and specific phobias, and generalized anxiety) disorder diagnoses were assessed at W1 (Grant, Hasin et al., 2005; Grant, Stinson et al., 2005). DSM–IV primary diagnoses excluded substance- and illness-induced cases; MDD diagnoses ruled out bereavement. Lifetime posttraumatic stress disorder (PTSD) and attention-deficit/hyperactivity disorder (ADHD) were assessed at W2 (Ruan et al., 2008) but considered as predictors herein only if prevalent up to W1.
All DSM–IV personality disorders (PDs) were assessed on a lifetime basis: avoidant, dependent, obsessive–compulsive, paranoid, schizoid, histrionic, and antisocial PDs at W1 (Grant, Hasin, Stinson et al., 2004); borderline, schizotypal, and narcissistic PDs at W2 (Grant et al., 2008). Test–retest reliabilities of AUDADIS-IV mood and anxiety (κ = .42-.65), PD (κ = .40-.71), and ADHD (κ = .71) diagnoses were fair to good (Grant, Dawson et al., 2003; Ruan et al., 2008). Convergent validity of mood, anxiety, and PD diagnoses was good to excellent (Grant, Hasin, Stinson et al., 2004; Grant, Hasin et al., 2005; Grant, Stinson et al., 2005).
Statistical Analyses
Incidence of each AUD and DUD was estimated as a percentage, the numerator comprising individuals with no lifetime history of the target disorder (e.g., alcohol dependence) at W1 who developed it during follow-up and the denominator comprising all respondents with no lifetime history of the disorder at W1 (population at risk). Individuals with alcohol dependence can later develop abuse, though this has not been observed for DUDs (Grant et al., 2009). Nevertheless, the hierarchical preemption under DSM–IV by dependence of subsequent abuse was suspended for both AUDs and DUDs.
Because of the low incidence of AUDs and DUDs, particularly among women, 3-year rates were considered so as to have sufficient cases for meaningful analyses. Incidence rates were compared by sociodemographic and psychiatric predictors, stratified on sex, using standard contingency table approaches. All sociodemographic predictors were entered simultaneously into sex-specific logistic regressions for each incident disorder.
Sex-specific logistic regressions estimated prediction of each incident AUD and DUD by specific psychiatric disorders, adjusted for sociodemographic variables and all other psychiatric disorders. Adjustment for diagnostic covariates tests the hypothesis that incidence is predicted by the pure (noncomorbid) form of a specific baseline disorder (Compton et al., 2007; Hasin et al., 2007). Odds ratios (ORs) were considered significant when their 95% confidence intervals excluded 1.00. When incidence is < 10%, as with AUDs and DUDs reported herein, the OR closely approximates the relative risk (Zhang & Yu, 1998).
Sex differences in ORs were assessed in models including Sex × Predictor Interaction Terms among the total sample, with alpha-to-stay = .05. No adjustments were made for multiple comparisons. All analyses utilized SUDAAN (Research Triangle Institute, 2008) to adjust for the NESARC’s complex sample design.
Results Alcohol Use Disorders
Three-year incidence ± SE of alcohol abuse among men and women was 8.39% ± 0.40 and 3.12% ± 0.19, respectively, chi-square(1) = 83.71, p < .0001; of alcohol dependence, 4.62% ± 0.23 and 2.18% ± 0.14, respectively, chi-square(1) = 56.03, p < .0001). Higher rates among men were observed in all sociodemographic subgroups examined (data available upon request). ORs for sociodemographic predictors of AUDs did not differ by sex (see Table 2), except for reduced incidence of dependence among Hispanic women, but no association in Hispanic men, versus non-Hispanic Whites.
Adjusteda Odds Ratios [95% Confidence Intervals] for 3-Year Incidence of DSM-IV Alcohol and Drug Use Disordersb by Wave 1 Sociodemographic Characteristics Among Male and Female NESARC Respondents
Adjusted ORs for prediction of AUDs by most psychiatric disorders did not differ by sex (see Table 3). A significant Sex × Bipolar I interaction identified reduced odds of abuse for men and no association for women. Though significant in both sexes, significantly greater ORs were observed among women than men for prediction of both AUDs by nicotine dependence. A significant Sex × Generalized Anxiety Disorder (GAD) interaction identified reduced odds of dependence for men and no association for women.
Odds Ratios [95% Confidence Intervals] for 3-Year Incidence of DSM-IV Alcohol and Drug Use Disordersa by Wave 1 Lifetime Psychiatric Diagnoses Among Male and Female NESARC Respondents Adjusted for Sociodemographic Characteristics and Additional Baseline Psychiatric Comorbidity
Drug Use Disorders
Three-year incidence ± SE of any drug abuse was 2.24% ± 0.19 among men and 1.22% ± 0.11 among women, chi-square(1) = 19.57, p < .0001; of any drug dependence, 1.16% ± 0.13 and 0.58% ± 0.09, respectively, chi-square(1) = 12.02, p = .0005. Similar to findings for AUDs, higher rates were observed among men in all sociodemographic subgroups (data available upon request); however, ORs for sociodemographic predictors of DUDs did not differ by sex (see Table 2).
Again similar to findings for AUDs, there were few sex differences in adjusted ORs for psychiatric predictors of DUDs (see Table 3). Interactions with sex were noted for prediction of abuse by schizotypal, borderline, avoidant, and obsessive–compulsive PDs. Schizotypal PD positively predicted abuse in women but not men. Borderline PD positively predicted abuse in both sexes, but more strongly in women; avoidant and obsessive–compulsive PDs negatively predicted abuse in men but not women.
The only sex difference in prediction of dependence was observed with obsessive–compulsive PD: reduced odds in men, no association in women.
DiscussionConsistent with our previous findings on lifetime prevalences and psychiatric comorbidity of AUDs and DUDs (Goldstein, Dawson, Chou, & Grant, 2012), incidence rates were higher in men, but there were few significant sex differences in predictors. Predictors could operate similarly despite differential prevalences, whether singly or in joint distributions, between men and women (cf. Huang et al., 2006). The only significant sex difference in sociodemographic predictors, reduced odds among Hispanic women but not Hispanic men, versus non-Hispanic Whites, for alcohol dependence, may reflect gendered norms regarding acceptability of alcohol use and associated behaviors (e.g., Zemore, 2007). Such norms may either protect against dependence, or reduce affected women’s willingness to report symptoms.
Significantly larger ORs were observed in women than men for prediction of AUDs but not DUDs by nicotine dependence. Although part of the externalizing spectrum, nicotine dependence is less informative about broader externalizing liability and less strongly related to the underlying externalizing dimension than antisocial PD and dependence on other substances (Markon & Krueger, 2005). Predictive relationships may also have been subject to “ceiling effects” reflecting comparatively high baseline prevalence of nicotine dependence (men: 20.0%; women: 15.6%).
Borderline PD predicted drug abuse significantly more strongly among women than men. This PD loads on both the internalizing subfactor of distress and the externalizing factor of psychopathology (Eaton et al., 2011). Previous studies have located DUDs at a more severe point than AUDs along the externalizing spectrum (Carragher et al., 2014; Kendler, Prescott, Myers, & Neale, 2003; Krueger, Markon, Patrick, & Iacono, 2005), and prevalences of most externalizing disorders are lower among women than men (Grant & Weissman, 2007). The stronger predictive relationships of borderline PD to incident DUDs among women, despite lack of sex differences in borderline PD prevalence (Grant et al., 2008), may thus reflect higher concentrations of externalizing liability among women than men with borderline PD, and women’s greater vulnerability to more severe externalizing pathology.
ORs for schizotypal PD and drug abuse were also higher in women. Previous studies identified relationships between use and disorders associated with cannabis, the most commonly used drug in the NESARC sample, and schizophrenia spectrum disorders, including schizotypal PD (Davis, Compton, Wang, Levin, & Blanco, 2013; Di Forti, Morrison, Butt, & Murray, 2007; Schiffman, Nakamura, Earleywine, & LaBrie, 2005; Stefanis et al., 2014). However, the directionality of those relationships could not be determined because the studies were cross-sectional. Respondents with schizotypal PD may have been more likely to use drugs before W1, becoming diagnosable with a DUD only during follow-up. To our knowledge, no studies have identified plausible explanations for the sex differences we observed in predictive relationships.
Finally, avoidant PD negatively predicted drug abuse and obsessive–compulsive PD negatively predicted both DUDs in men but not women. With lower prevalence in men (Grant, Hasin, Stinson, et al., 2004), essential features including hypersensitivity to negative evaluation, and correlates including high harm avoidance (Joyce et al., 2003), avoidant PD may protect men from socially disvalued behaviors like problematic drug use, for which they are otherwise at greater risk than women. Similarly, although prevalence of obsessive–compulsive PD does not differ by sex (Grant, Hasin, Stinson, et al., 2004), its essential features including scrupulosity, overconscientiousness, and inflexibility about morality and values may deter DUD development more strongly among men.
Study limitations include its reliance on self-reports. Collateral data sources are particularly important for PD assessment to mitigate potential distortions in respondents’ self-appraisals, including lack of insight into the effects of symptomatic behaviors on role functioning (Clark, 2007; Pedersen, Karterud, Hummelen, & Wilberg, 2013; Zimmerman, 1994). Additionally, the 3-year follow-up yielded relatively few incident cases of AUDs and DUDs, particularly among women.
The NESARC sample was limited by design to general population U.S. adults. Therefore, the applicability of these findings to other populations, and to individuals < 18 years old at baseline in the general U.S. population, is unclear. Moreover, respondents youngest at W2 were 20 years old, beyond the ages of peak hazards for AUDs and DUDs (Compton et al., 2007; Hasin et al., 2007). That relationships between specific baseline disorders and incident AUDs and DUDs may vary across developmental phases (Grant et al., 2009), including by sex, could explain our unexpected findings of reduced risks of incident alcohol abuse among men with bipolar I, of alcohol dependence among men with GAD, of drug abuse among men with avoidant and obsessive–compulsive PDs, and of drug dependence among men with obsessive–compulsive PD. Future longitudinal studies should involve longer follow-up periods, consider including institutional subsamples, and capture earlier developmental phases.
Ideally, all diagnostic predictors would have been assessed at W1, but respondent and interviewer burden made it infeasible. Mitigating this concern for PDs, respondents were explicitly queried about symptoms occurring most of the time, throughout their lives, regardless of the situation or whom they were with (Grant, Hasin, Stinson, et al., 2004). Respondents with each PD were also more impaired than those with no PD on the Mental Component Summary score of the Short Form 12-Item Health Survey, version 2 (Gandek et al., 1998), and more often reported stressful life events such as relationship breakups and financial, interpersonal, or employment problems, at each wave, regardless of when specific PDs were assessed (Skodol et al., 2011).
The tendency of respondents to recall and report onsets as more recent than they actually were for Axis I disorders (Prusoff, Merikangas, & Weissman, 1988), substance use (Johnson & Schultz, 2005), and medical conditions (Raphael & Marbach, 1997) may likewise mitigate time-of-assessment concerns for PTSD. While we are unaware of findings documenting forward telescoping specifically in PTSD, its occurrence is plausible given findings on other disorders.
The few sex differences we identified in sociodemographic and diagnostic predictors of AUD and DUD incidence were modest, yielding limited implications for sex-specific targeting of prevention and early identification efforts. Nevertheless, AUDs and DUDs and their predictors confer substantial burdens on affected individuals, their social networks, and health, social service, and correctional systems (Brown, 2010; Sirotich, 2009; Whiteford et al., 2013). Treatment utilization for AUDs and DUDs is low, despite the availability of a growing range of empirically supported therapies (Compton et al., 2007; Hasin et al., 2007). Despite the lack of strong sex-specific signals, this study’s findings, together with those of previous prospective studies (e.g., de Graaf et al., 2013; Grant et al., 2009; Fergusson, Horwood, & Ridder, 2007) reinforce the need for comprehensive assessment and evidence-based treatment of mental health and substance use disorders in both sexes, regardless of clients’ chief complaints and the clinical settings to which they present.
Prevalences are similar (Dawson, Goldstein, & Grant, 2013; Goldstein et al., 2015) and concordances excellent between DSM–IV dependence and DSM-5 moderate to severe AUDs and DUDs (Compton, Dawson, Goldstein, & Grant, 2013; Goldstein et al., in press). These findings plus similarity in clinical profiles between alcohol dependence and DSM-5 moderate to severe AUD (Dawson et al., 2013) suggest that predictors may be similar across these disorders, despite increases in sociodemographic and substance use diversity of the population since the W1 and W2 NESARC data were collected. Conversely, divergence of clinical profiles (Dawson et al., 2013) suggests caution in extrapolating from abuse to mild DSM-5 disorders. Prospective research is needed to identify sociodemographic and diagnostic predictors of and mechanisms underlying incident DSM-5 AUDs and DUDs. Whether there are sex differences in predictive relationships across developmental phases, the role of additional comorbidity in etiology and course, perceived need and barriers to treatment for AUDs and DUDs, and outcomes of prevention and treatment also warrants examination.
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Submitted: December 11, 2014 Revised: February 23, 2015 Accepted: March 3, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 924-932)
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Record: 57- Stimulant medication use in college students: Comparison of appropriate users, misusers, and nonusers. Hartung, Cynthia M.; Canu, Will H.; Cleveland, Carolyn S.; Lefler, Elizabeth K.; Mignogna, Melissa J.; Fedele, David A.; Correia, Christopher J.; Leffingwell, Thad R.; Clapp, Joshua D.; Psychology of Addictive Behaviors, Vol 27(3), Sep, 2013 pp. 832-840. Publisher: American Psychological Association; [Journal Article] Abstract: While stimulant medication is commonly prescribed to treat Attention-Deficit/Hyperactivity Disorder in children and adolescents (Merikangas, He, Rapoport, Vitiello, & Olfson, 2013; Zuvekas & Vitiello, 2012) and is considered an empirically supported intervention for those groups (Barkley, Murphy, & Fischer, 2008; Pelham & Fabiano, 2008; Safren et al., 2005) surprisingly little is known about the efficacy of stimulants in the slightly older emerging adult population. A focus has emerged, however, on illicit stimulant use among undergraduates, with studies suggesting such behavior is not uncommon (e.g., Arria et al., 2013). Unfortunately, details are lacking regarding outcomes and personal characteristics associated with different patterns of stimulant misuse. The current study compares the characteristics of four groups of college students, including those with stimulant prescriptions who use them appropriately (i.e., appropriate users), those who misuse their prescription stimulants (i.e., medical misusers), those who obtain and use stimulants without a prescription (i.e., nonmedical misusers), and those who do not use stimulant medications at all (i.e., nonusers). Undergraduates (N = 1,153) from the Southeastern, Midwest, and Rocky Mountain regions completed online measures evaluating patterns of use, associated motives, side effects, ADHD symptomatology, and other substance use. Both types of misusers (i.e., students who abused their prescriptions and those who obtained stimulants illegally) reported concerning patterns of other and combined substance use, as well as higher prevalence of debilitating side effects such as insomnia and restlessness. Research and practical implications are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Stimulant Medication Use in College Students: Comparison of Appropriate Users, Misusers, and Nonusers / BRIEF REPORT
By: Cynthia M. Hartung
Department of Psychology, University of Wyoming;
Will H. Canu
Department of Psychology, Appalachian State University
Carolyn S. Cleveland
Department of Psychology, University of Wyoming
Elizabeth K. Lefler
Department of Psychology, University of Northern Iowa
Melissa J. Mignogna
Department of Psychology, Oklahoma State University
David A. Fedele
Department of Psychology, University of Florida
Christopher J. Correia
Department of Psychology, Auburn University
Thad R. Leffingwell
Department of Psychology, Oklahoma State University
Joshua D. Clapp
Department of Psychology, University of Wyoming
Acknowledgement: We thank Erica K. Allen and Collin T. Scarince for their contributions to this project.
Studies estimate a 4–14% yearly incidence of nonprescribed stimulant medication use in college students (American College Health Association [ACHA], 2010; Hall, Irwin, Bowman, Frankenberger, & Jewett, 2005; McCabe, Teter & Boyd, 2006; Weyandt et al., 2009; White, Becker-Blease, & Grace-Bishop, 2006), which is higher than the national prevalence of cocaine, hallucinogen, or inhalant use (SAMHSA, 2011), and approximately double the prevalence of prescribed stimulant use (2–3%; Babcock & Byrne, 2000; Stone & Merlo, 2011) in this age group. In considering stimulant abuse, however, it is important to note that not all who use illicitly are qualitatively similar. While motive (e.g., getting high vs. increasing concentration) is one way to categorize stimulant users (Teter, McCabe, Cranford, Boyd & Guthrie, 2005), means and degree of use differentiate among (a) medical misusers (i.e., those with a prescription who periodically use excessive doses), (b) nonmedical misusers (i.e., those who obtain and use stimulants illegally), and (c) appropriate users (i.e., those who use prescription according to instructions). The need for closer examination of these groups is underscored by the somewhat ambiguous stimulant-related maladjustment (Bogle & Smith, 2009), and infrequent and incomplete differentiation among misuser groups in the literature.
Although prevalence estimates vary widely (e.g., 4%, McCabe, Knight, Teter, & Wechsler, 2005; 38%, Arria et al., 2013; 43%, Advokat, Guidry, & Martino, 2008), it seems likely that a substantial number of college students misuse stimulants (DeSantis, Webb, & Noar, 2008). In contrast to prescribed use of stimulants in college students with Attention-Deficit/Hyperactivity Disorder (ADHD; DuPaul, Weyandt, O’Dell, & Varejao, 2009), which some have suggested ameliorates maladjustment (Staufer & Greydanus, 2005), nonmedical misuse is correlated with lower grades (McCabe et al., 2005), academic concerns (Rabiner et al., 2009), risk for polysubstance abuse (Rozenbroek & Rothstein, 2011), and a desire to improve studying (Stone & Merlo, 2011). However, unaddressed symptoms of ADHD may be linked to nonmedical misuse of stimulants too, with one study finding that 12% of nonmedical misusers believed they had the disorder (Advokat et al., 2008). It is also possible that students without ADHD use stimulants to enhance academic performance (Smith & Farah, 2011), as staying awake and increasing studying efficiency are frequent rationales for misuse (Advokat et al., 2008).
While addressing undiagnosed or undertreated ADHD and related academic problems is a motive for misuse that parallels the intended purpose of prescription stimulants, recreation (i.e., euphoric effects; Teter et al., 2005) and socialization (White et al., 2006) are not uncommonly endorsed as reasons for use. This may be particularly prevalent in nonmedical misusers, as approximately one fifth of this group reports using stimulants while drinking (Low & Gendaszek, 2002), and to prolong intoxication (Rabiner et al., 2009). Some have suggested that stimulant use may be even more reinforcing in social situations, as the resulting alertness may facilitate prolonged social engagement (Hall et al., 2005). However, recreational motives for stimulant abuse do not outrank academic motives among nonmedical misusers, and are uncommonly the sole motive reported (Rabiner et al., 2009).
Specific personality characteristics have also been related to stimulant misuse, with both sensation seeking (Arria, Caldeira, Vincent, O’Grady & Wish, 2008) and perfectionism (Low & Gendaszek, 2002) positively predicting this behavior in college populations. Further, men appear more likely than women to misuse stimulants (Bogle & Smith, 2009; Hall et al., 2005; see exception in McCabe et al., 2005), which may be due to sex differences in risk-taking (Byrnes, Miller & Schafer, 1999) or knowledge about from whom one can illicitly obtain stimulants (Hall et al., 2005).
Immediate adverse consequences of stimulant use have been reported in college student nonmedical misuser samples, including appetite reduction (63%), sleep problems (60%), irritability (45%), and reduced academic self-efficacy (41%; Rabiner et al., 2009). Taken with the potential legal consequences of illicit use of a Schedule II substance (e.g., methylphenidate) and increased risk of polysubstance abuse, this suggests illicit stimulant use is associated with risk across several domains. However, particularly given some studies suggesting relatively mild and circumscribed maladjustment in misusing college students (e.g., Bogle & Smith, 2009), replication and further detailing of the putative adverse consequences associated with illicit stimulant use is a valid aim, especially given the potential downside of overly negative portrayals (e.g., Food and Drug Administration caps on production).
This study examined four college student groups differentiated by type of stimulant use (i.e., nonusers, nonmedical and medical misusers, appropriate users). Given the extant literature, hypotheses were as follows: (a) both misuser groups were expected to more frequently nominate recreational motives for stimulant use; (b) misusers, given their nonprescribed drug use, were expected to endorse high rates of other illicit substance use (i.e., concurrent to stimulant use or at other times in the past year); (c) nonmedical misusers would report more ADHD-related symptomatology (i.e., inattention, hyperactivity) than nonusers, but less than either appropriate users or medical misusers; (d) nonmedical misusers would be distinguished by high sensation seeking and perfectionism. Finally, other planned analyses examined whether groups differed on other motives for use, side effects, and methods of ingestion; however, given a relative dearth of direction from prior research for these variables, specific hypotheses were not made.
Method Participants
Participants were 1,153 undergraduates (65.2% female; 88.4% European American) from four public universities located in the Southeast (n = 2), Rocky Mountain (n = 1), and Midwest (n = 1) regions of the United States who were compensated with class credit. The mean age of these participants was 19.72 years (SD = 1.45; range: 18–25). Distribution by class standing was 46.2% freshmen, 24.0% sophomores, 16.8% juniors, and 13.0% seniors. Based on self-reported stimulant use, groups included (a) nonusers (n = 708), (b) nonmedical misusers (i.e., illicitly obtaining and using stimulant medication without a prescription; n = 274), (c) appropriate users (i.e., taking stimulants according to prescription; n = 146), and (d) medical misusers (i.e., using higher doses or more frequently than prescribed; n = 25). Agreement regarding group assignment was 100% (consensus of first, second, and fourth authors). At two of four universities, stimulant users were overselected via a prescreening questionnaire. Thus, the distributions across user status do not reflect the true prevalence of use and misuse on these college campuses.
Measures and Procedure
Participants completed all rating scales online in a fixed order after providing informed consent. Study procedures were approved by each university’s Institutional Review Board.
Substance use
Participants reported whether they used a variety of legal and illegal substances in the past year (e.g., alcohol, cigarettes, marijuana). They also reported whether they used substances concurrently with prescription stimulants. Previous studies support the reliability and validity of self-reported substance use (Tucker, Murphy, & Kertesz, 2010), and endorsement of 12-month substance use or nonuse is also consistent with prior research in this area (e.g., Johnston, O’Malley, Bachman, & Schulenberg, 2013; Mohler-Kuo, Lee, & Wechsler, 2003; SAMHSA, 2011).
Stimulant use
Students were asked about: (a) use (e.g., “I have a prescription and take accordingly”; “I do not have a prescription but obtain stimulants and use them”; see White et al., 2006), (b) source for obtaining (e.g., received from my doctor/pharmacy, given by a friend/family member, or bought or stolen from someone; based on McCabe, Teter, & Boyd, 2006), (c) method of ingestion (e.g., oral, intranasal, or intravenous; as per Teter et al., 2005), (d) reasons for use (e.g., control ADHD symptoms, suppress appetite, or stay awake; adapted from Low & Gendaszek, 2002), and (e) side effects experienced while taking stimulants (e.g., insomnia, loss of appetite, or weight loss).
ADHD symptoms
ADHD symptoms were measured with an 18-item self-report measure of DSM–IV inattention and hyperactivity (Barkley & Murphy, 2006). Participants indicated whether they never/rarely (0), sometimes (1), often (2), or very often (3) experienced each symptom. Summary scores were created for inattention and hyperactivity. Internal consistency has been good for inattention (α = .80) and adequate for hyperactivity (α = .73) based on college student self-reports (e.g., Fedele, Hartung, Canu, & Wilkowski, 2010). In addition, interrater reliability has been found to be moderately high in adults (e.g., r = .67; Barkley, Knouse, & Murphy, 2011). Convergent and discriminant validity have also been demonstrated for adult self-reports (e.g., Magnusson et al., 2006). Internal consistency in the current sample was good for inattention (α = .87) and adequate for hyperactivity (α = .76).
Personality characteristics
Sensation seeking was measured using a 16-item version (Donohew et al., 2000) of the Sensation Seeking Scale (Zuckerman, 1994). Responses were disagree a lot (0), disagree a little (1), don’t agree or disagree (2), agree a little (3), or agree a lot (4) and were aggregated into a summary score (range = 0 to 64). Previous reports of internal consistency were adequate (α = .79; Donohew et al., 2000) and internal consistency was good in the current sample (α = .82). Perfectionism was measured using a 24-item version (Khawaja & Armstrong, 2005) of the Frost Multi-Dimensional Perfectionism Scale (Frost, Marten, Lahart, & Rosenblate, 1990). This version has been reported to have excellent internal consistency (α = .90) and strong concurrent validity with other measures of perfectionism (Khawaja & Armstrong, 2005). Responses range from strongly disagree (0) to strongly agree (4). There are four subscales: concern over mistakes (10 items), organization (4 items), parental expectations (6 items), and high personal standards (4 items). Internal consistency was adequate for parental expectations (α = .79), good for organization (α = .88) and concern over mistakes (α = .87), but inadequate for high personal standards (α = .64). Accordingly, the latter was omitted from analyses.
ResultsMultinomial logistic regression analyses were conducted to examine relations between predictors and user status. For some analyses, all four user status groups were included. For other analyses, nonusers were not included because the items were not relevant (e.g., reasons for use, side effects). For all analyses, sex and university were entered as covariates due to significant differences across user status. In keeping with prior findings (e.g., Bogle & Smith, 2009), men were more likely to engage in nonmedical misuse than women (p = .009). Alpha corrections were conducted for all analyses and resulting p values are noted in each of the tables. For each regression, likelihood (i.e., χ2) and pairwise odds ratios representing the unique relation between predictor and outcome variable (i.e., user status) are reported.
First, a logistic regression analysis was conducted to examine the relation between reasons for stimulant use and user status (see Table 1). We were particularly interested in using “to get high” as a measure of recreational use. However, we were not able to include this reason in the regression due to low levels of endorsement. Specifically, 13% of nonmedical misusers and 24% of medical misusers indicated using stimulants to get high (compared to none of appropriate users). With regard to other reasons for use, we conducted planned exploratory analyses. Results showed that both types of misusers endorsed some reasons significantly more often than appropriate users. Specifically, nonmedical and medical misusers were more likely to endorse using to stay awake than appropriate users. Also, nonmedical misusers were more likely to report using to study than appropriate users whereas medical misusers were more likely to endorse using to increase academic performance than appropriate users. Finally, both appropriate users and medical misusers were more likely to use “to control ADHD symptoms” than nonmedical misusers.
Multinomial Logistic Regression Analysis for Reasons for Stimulant Use by User Status
Another logistic regression analysis was conducted to examine the relation between use of other substances and user status (see Table 2). Across eight substances, nonusers of stimulants were the least likely to endorse use of other substances, appropriate users were next in terms of likelihood to endorse, and misusers were the most likely to endorse. Although alcohol use was surveyed, it could not be entered in the regression because 100% of medical misusers endorsed it.
Multinomial Logistic Regression Analyses for Use of Other Substances
Next, a logistic regression analysis was conducted to examine the relation between concurrent use of stimulants with other substances and user status (see Table 3). Appropriate users were typically the least likely to endorse concurrent use of additional substances. Medical misusers were significantly more likely to endorse concurrent marijuana use than appropriate users. Nonmedical misusers were more likely to endorse concurrent marijuana and pain medication use than appropriate users. Interestingly, nonmedical misusers were significantly less likely to endorse concurrent alcohol use than appropriate users.
Multinomial Logistic Regression Analyses for Concurrent Use of Other Substances and Stimulants
Next, regressions were conducted to examine how user status related to ADHD and personality variables (see Table 4). Nonusers reported significantly lower levels of inattention and hyperactivity than any other group. In addition, nonmedical misusers reported lower levels of inattention than appropriate users and lower levels of hyperactivity than medical misusers. With regard to personality, nonmedical misusers reported higher parental expectations than nonusers and appropriate users. Moreover, nonmedical misusers reported higher levels of sensation seeking than appropriate users and nonusers.
Multinomial Logistic Regression Analyses for (A) Inattention & Hyperactivity and (B) Perfectionism & Sensation Seeking by User Status
Exploratory analyses were conducted to examine differences across user groups for side effects, stimulant source, and ingestion. An analysis was conducted to examine the relation between side effects and user status (see Table 5). Overall, misusers appeared to experience more side effects; both misuser groups were significantly more likely to endorse exaggerated well-being and restlessness than appropriate users. In addition, nonmedical misusers were more likely to report insomnia and exaggerated well-being—and less likely to report weight loss, anxiety, or gastrointestinal problems—than appropriate users. Finally, medical misusers were more likely to endorse changes in sex drive than nonmedical misusers.
Logistic Regression Analysis for Various Side Effects of Stimulant Medication by User Status
Finally, sources for obtaining stimulants and ingestion methods were examined. No regression analysis could be conducted for these variables because appropriate users obtained their stimulants exclusively from prescriptions and participants reported oral ingestion as their primary method. Notably, among nonmedical misusers, 81% got stimulants from a friend, 45% bought them, and 4% stole them. Additionally, nasal ingestion among nonmedical (17.9%) and medical misusers (20.0%) was much higher than for appropriate users (0.0%) although the difference between the two misuser groups was not significant.
DiscussionThe purpose of this study was to compare characteristics of undergraduates who use, misuse, and do not use prescription stimulants. Overall, those classified as misusers (i.e., medical and nonmedical) presented relatively more concerning correlates than those who used stimulants according to prescription. First, although not statistically analyzed due to nonendorsement by all appropriate users, both medical and nonmedical misusers more frequently equate stimulant ingestion with recreation (i.e., getting high). Further, misusers appeared to experience different side effects. Notably, both misuser groups were more likely to endorse exaggerated well-being and restlessness than appropriate users. Nonmedical misusers were more likely to endorse insomnia than appropriate users, but less likely to have experienced anxiety, weight loss, or digestive problems. Unfortunately, “desirable” side effects (e.g., exaggerated well-being) may encourage misuse by off-setting negative consequences and reinforcing the expectation of euphoria.
Perhaps not surprisingly, misusers reported the highest rates of other substance use. Nonmedical misusers were more likely to report use of marijuana and hallucinogens than nonusers and appropriate users. Medical misusers were the most likely endorsers for all substances but these differences only reached statistical significance when compared to nonusers for cigarettes, amphetamines, and anxiety medication. When examining substances frequently used by college students (e.g., alcohol and marijuana; ACHA, 2010), appropriate users were more likely than nonusers to endorse use of these substances. This finding is consistent with prior research suggesting that ADHD is associated with increased risk for substance use (Wilens, 2004), but seems to contradict a documented protective effect of stimulant treatment (Biederman, 2003; Faraone & Wilens, 2003; Wilens, Faraone, Biederman, & Bunawardene, 2003). However, the current data cannot inform the prospective influence of stimulant intervention in childhood. Overall, it seems reasonable to conclude that stimulant misuse is associated with risk for broader substance use.
With regard to concurrent substance use, misusers were more likely than appropriate users to report marijuana use in combination with stimulants. In addition, nonmedical misusers were significantly more likely to endorse concurrent pain medication use than appropriate users. Such recreational use suggests that the motives of misusers may not be benign (e.g., extra dose for finals). This is consistent with other studies in which students frequently endorsed using stimulants while “partying” (e.g., Teter et al., 2005; White et al., 2006), and those in which short-term positive gain is reported with stimulant misuse (Rabiner et al., 2009) despite low endorsement of long-term academic gain (Hall et al., 2005).
With regard to concurrent alcohol use, the three user groups reported relatively high rates, which is troubling due to potential interactions between alcohol and stimulants. Specifically, using stimulants in combination with alcohol may diminish the experience of alcohol-related effects. This may in turn lead to underestimation of inebriation (Flack et al., 2007; Hingson, Edwards, Heeren & Rosenbloom, 2009; Knight et al., 2002) and poor decisions (e.g., drunk driving, unsafe sexual activity) that could lead to physical harm (e.g., motor vehicle accident, sexually transmitted disease, unplanned pregnancy).
Regarding inattention, nonmedical misusers reported significantly lower levels than appropriate users but higher levels than nonusers. For hyperactivity, nonmedical misusers reported significantly lower levels than medical misusers, but higher levels than nonusers. Thus, nonmedical misusers may be using stimulants to address subthreshold ADHD, and self-medication may be a viable explanation for the behavior of some nonmedical misusers (Rabiner et al., 2009). Misusers endorsed levels of sensation seeking that were significantly higher than nonusers and appropriate users. This is consistent with research linking sensation seeking to substance abuse (Carlson, Johnson & Jacobs, 2010; Dunlop & Romer, 2010; Zuckerman, 1994). Group differences on perfectionism subscales were also evident. Most notable, perhaps, was that nonmedical misusers endorsed higher perceived parental pressure relative to nonusers. Thus, perception of parental expectations for academic success may moderate the misuse of stimulants among those without a prescription. When asked about sources for obtaining stimulants, 81% of nonmedical misusers reported getting them from friends, closely resembling previous findings (77.8%; Barrett, Darredeau, Bordey, & Pihl, 2005). This suggests that some—and potentially many—college students with prescription stimulants are taking their medication in smaller doses or less often than prescribed as there seem to be “leftovers” available to sell or share.
Limitations
First, the medical misuser group was small (n = 25), and this limited power to detect differences between this and other groups. Given that this group reported very high rates of problematic consequences that were often not statistically significantly different from other groups, more research with individuals who misuse stimulant prescriptions is warranted. Next, our assessments of substance use and ADHD symptoms were limited to self-report measures, and future research might use corroborating sources (e.g., biochemical and parent-report measures, respectively). Another limitation was related to reports of type and dose of stimulants. We attempted to gather this information but participant responses reflected confusion or lack of knowledge. Further, data regarding frequency of misuse, duration of use, and amount typically consumed are lacking. Future research should address such details to extend our appreciation for differences among user groups. Another limitation was related to the overselection of stimulant users, which increased power but decreased representativeness. Further, although the current data were derived from four universities, the findings may not fully generalize to groups underrepresented in this sample (see McCabe, Teter, & Boyd, 2004). Finally, while geographic region and Greek affiliation have been shown to potentially add to risk for illicit stimulant use in college (McCabe et al., 2005), we did not consider the impact of these variables in the current study; researchers should include these in the design of future studies.
Conclusions
These findings reinforce that the misuse of stimulants is associated with other risks, such as that for polysubstance misuse. However, stimulant misuse by itself, even for academic reasons, may have concerning side effects (Graham et al., 2011). One university’s decision to change its honor code to include stimulant misuse as an “improper assistance” violation indirectly supports the call to proactively address this issue (Arria & DuPont, 2010; Diller, 2010; Wilens et al., 2008). Additionally, roughly 14% of students in this sample misused a prescription. Further, 81% of nonmedical misusers obtained stimulants from a friend. These two findings emphasize the importance of prescribers closely monitoring consumption and openly discussing consequences of misuse and diversion with college students. For example, if a student reports only taking medication on weekdays, then 30 pills might last 6 weeks rather than 4. Therefore, prescribers may want to evaluate how often students are taking their medication and prescribe accordingly to reduce the quantity of stimulants available to be diverted.
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Submitted: January 5, 2012 Revised: May 16, 2013 Accepted: June 4, 2013
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Source: Psychology of Addictive Behaviors. Vol. 27. (3), Sep, 2013 pp. 832-840)
Accession Number: 2013-33297-006
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Record: 58- Stimulus control in intermittent and daily smokers. Shiffman, Saul; Dunbar, Michael S.; Ferguson, Stuart G.; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 847-855. Publisher: American Psychological Association; [Journal Article] Abstract: [Correction Notice: An Erratum for this article was reported in Vol 29(4) of Psychology of Addictive Behaviors (see record 2015-33590-001). There was an error in the reported value in the Discussion section. The second sentence of the second paragraph should have read, 'Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations.' This was a result of a transcription error. All versions of this article have been corrected.] Many adult smokers are intermittent smokers (ITS) who do not smoke daily. Prior analyses have suggested that, compared with daily smokers (DS), ITS smoking was, on average, more linked to particular situations, such as alcohol consumption. However, such particular associations assessed in common across subjects may underestimate stimulus control over smoking, which may vary across persons, due to different conditioning histories. We quantify such idiographic stimulus control using separate multivariable logistic regressions for each subject to estimate how well the subject’s smoking could be predicted from a panel of situational characteristics, without requiring that other subjects respond to the same stimuli. Subjects were 212 ITS (smoking 4–27 days/month) and 194 DS (5–30 cigarettes daily). Using ecological momentary assessment, subjects monitored situational antecedents of smoking for 3 weeks, recording each cigarette in an electronic diary. Situational characteristics were assessed in a random subset of smoking occasions (n = 21,539), and contrasted with assessments of nonsmoking occasions (n = 26,930) obtained by beeping subjects at random. ITS showed significantly stronger stimulus control than DS across all context domains: mood, location, activity, social setting, consumption, smoking context, and time of day. Mood and smoking context showed the strongest influence on ITS smoking; food and alcohol consumption had the least influence. ITS smoking was under very strong stimulus control; significantly more so than DS, but DS smoking also showed considerable stimulus control. Stimulus control may be an important influence on maintaining smoking and making quitting difficult for all smokers, but especially among ITS. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Stimulus Control in Intermittent and Daily Smokers
By: Saul Shiffman
Department of Psychology, University of Pittsburgh;
Michael S. Dunbar
Department of Psychology, University of Pittsburgh
Stuart G. Ferguson
School of Medicine, University of Tasmania
Acknowledgement: This work was supported by Grant R01-DA020742 (Shiffman) from the United States Department of Health and Human Services, National Institutes of Health, National Institute on Drug Abuse.
Nicotine dependence is considered the primary determinant of persistent cigarette smoking. This helps explain why most smokers smoke frequently throughout the day, every day, which functions to prevent nicotine levels from sinking below a level where nicotine withdrawal sets in (Benowitz, 2010; Stolerman & Jarvis, 1995). Maintaining nicotine levels (“trough maintenance;” Russell, 1971) is best accomplished by smoking at very regular intervals, but some models allow room for variations from this, for example, smoking in response to situational cues. However, this leeway is limited, as nicotine withdrawal can set in within a few hours of abstinence (Benowitz, 2008).
Nondaily smoking is becoming increasingly prevalent among U.S. adults, however (Cooper et al., 2010; Schane, Glantz, & Ling, 2009; Shiffman, 2009b; Shiffman, Tindle, et al., 2012). As many as 38% of U.S. adult smokers are now nondaily or intermittent smokers (ITS; U.S. Department of Health & Human Services, 2014). ITS smoke an average of 4–5 cigarettes per day on the days they smoke (Gilpin, Cavin, & Pierce, 1997; Shiffman, Tindle, et al., 2012; Wortley, Husten, Trosclair, & Chrismon, & Pederson, 2003), but their defining characteristic is that they frequently go for several days running without smoking (Shiffman, Tindle, et al., 2012), and thus clearly do not maintain nicotine levels (Benowitz, 2008). ITS may be seeking the reinforcing effects of acute doses of nicotine (“peak seeking”; Russell, 1971), rather than trying to maintain a minimal level to avoid withdrawal (“trough avoidance”). Nor is this necessarily just a transient phase en route to dependence: Zhu, Sun, Hawkins, Pierce, and Cummins (2003) reported that a substantial proportion of ITS maintained that status over two years. Similarly, we have studied a sample of ITS who have been smoking for an average of 19 years, and have consumed an average of more than 40,000 cigarettes (Shiffman, Tindle, et al., 2012), yet show little or no dependence (Shiffman, Ferguson, Dunbar, & Scholl, 2012; Shiffman, Tindle, et al., 2012). Nevertheless, ITS have surprising trouble quitting smoking, with failure rates of 78%, only slightly lower than those of daily smokers (DS; Tindle & Shiffman, 2011).
What might account for the difficulty ITS have quitting? One factor might be stimulus control. A behavior (in this case, smoking) is said to be under stimulus control when the presence of a given stimulus (or stimuli) changes the likelihood of that behavior occurring. Such relationships are believed to be established through various learning processes. Stimuli could influence smoking by serving as discriminative stimuli, indicating that smoking will be reinforcing, acting as priming stimuli, and/or as conditioned stimuli eliciting responses instilled by prior associations with stimuli, including the effects of smoking itself (Bickel & Kelly, 1988). If ITS smoking is strongly associated with certain situational cues, exposure to such cues might promote continued smoking and pose a significant barrier to abstinence in the face of exposure to relevant cues. Strong stimulus control is a common feature of casual drug use (Bickel & Kelly, 1988), and we have hypothesized that its diminution is an important step in the development of tobacco dependence (Shiffman & Paty, 2006; Shiffman, Waters, & Hickcox, 2004), as use shifts from particular settings to nicotine maintenance via frequent nicotine intake. Consistent with this, in responses to a questionnaire on a scale assessing smoking motives (Piper et al., 2004), ITS have identified responsiveness to cues as their most important motivation to smoke (Shiffman, Dunbar, Scholl, & Tindle, 2012), and smoking in chippers—very light smokers—has been shown to be under greater stimulus control than that seen in heavy smokers (Shiffman & Paty, 2006).
A useful way to assess individuals’ smoking patterns is via ecological momentary assessment (EMA; Shiffman, 2009a, in press; Stone & Shiffman, 1994)—collection of real-time, real-world data on multiple occasions. Collecting data in subjects’ real-world settings ensures ecological validity, and collecting it in real time avoids problems of recall bias. Collecting data on both smoking and nonsmoking occasions allows one to characterize the associations between smoking and situational antecedents (Paty, Kassel, & Shiffman, 1992; Shiffman, 2009a). This method has been used to study situational associations with smoking in a variety of populations (e.g., Beckham et al., 2008; Cronk & Piasecki, 2010; Mermelstein, Hedeker, Flay, & Shiffman, 2007; Shiffman et al., 2002; Shiffman & Paty, 2006).
We recently used EMA data to compare the particular stimuli associated with smoking for ITS and DS, and found that ITS smoking was more likely to be associated with cues such as being away from home, being in a bar, drinking alcohol, socializing, being with friends and acquaintances, and being where others were smoking (Shiffman et al., 2014a). However, these analyses, although they contribute to our understanding of ITS smoking, only identify the smoking triggers that most ITS share in common; they do not fully reflect the degree of control that various stimuli exert over individual ITS smoking.
To quantify stimulus control, one must abstract from relationships between smoking and the cues that ITS (or DS) share in general, to examine idiographic associations with cues within each person, as these associations can be idiosyncratic, with different smokers having different, even opposite, reactions to the same cue. For example, if some subjects smoke when they are feeling good, whereas others smoke when they feel bad, a group-wise analysis of individual moods might show no effect, even though mood exercises stimulus control over smoking for both groups of subjects. Indeed, data from an EMA study of DS showed such effects, in that the overall group-wise relationship between smoking and mood was estimated as 0 (Shiffman et al., 2002), yet the distribution showed wide variation, with relationships in both directions, and these variations proved meaningful in predicting subsequent relapse (Shiffman et al., 2007). Also, different subjects might respond to different stimuli, even within a given domain. For example, some subjects might respond to how good or bad they feel and others to how aroused they feel. Both might be considered equally under stimulus control by mood, but these associations, too, would be missed or diluted in the analyses of single cues that are typically done (e.g., Shiffman et al., 2014a). Yet such variable idiographic relationships between smoking and antecedent stimuli are to be expected if the associations are due to conditioning (Niaura et al., 1988), because individuals’ learning histories would likely vary.
Accordingly, in this paper we go beyond assessing directional group-wise associations between situational stimuli and smoking to quantify the degree of stimulus control using EMA data to estimate how well various situational characteristics can account idiographically for each individual’s smoking based on analyses within each individual subject. Subsequent comparisons of the resulting parameters are made between ITS and DS.
Method Subjects
Subjects were 212 ITS and 194 DS recruited through advertisement. Participants had to be at least 21 years old, report smoking for at least 3 years, smoking at their current rate for at least 3 months, and not planning to quit within the next month. DS had to report smoking every day, averaging 5 to 30 cigarettes per day. ITS had to report smoking 4 to 27 days per month, with no restrictions on number of cigarettes. We oversampled African American smokers, because national surveys have indicated they are more likely to be ITS (Trinidad et al., 2009); data were weighted to balance ethnic representation. Analyses of association of smoking and particular cues were reported for this sample in Shiffman et al. (2014a), and the sample largely overlaps with one reported in several analyses of other data (Shiffman et al., 2013a; Shiffman et al., 2013b; Shiffman et al., 2012; Shiffman, Ferguson, et al., 2012; Shiffman, Tindle, et al., 2012).
Briefly, DS were 41 years old, 55% male, smoked 15 cigarettes per day, and had been smoking for 26 years on average. ITS were slightly younger (37 years old), 49% male, smoked 4–5 cigarettes per day on smoking days, smoked 4–5 days per week, and had been smoking for 19 years on average (see Shiffman et al., 2014a for additional details).
Procedures
The EMA methods for this study have been described in detail in Shiffman et al. (2014a), and are similar to those in previous studies (Ferguson & Shiffman, 2011; Shiffman, 2009a; Shiffman et al., 2002). Briefly, subjects were provided with a palmtop computer, which they used to monitor smoking for three weeks (average 21.60 ± 4.11 days). Subjects were to record all cigarettes, but to avoid excessive burden, the computer administered an assessment of the surrounding circumstances only for a portion of those smoking occasions, selected at random.
To assess the circumstances of nonsmoking moments, as a necessary contrast for smoking occasions (Paty et al., 1992; Shiffman, 2009a), the computer “beeped” subjects at random about four times a day (but never within 15 min of smoking), and administered a nearly identical assessment. Subjects in the analysis received three to four prompts per day on average (DS: M = 3.52; ITS: M = 3.93) and responded to 88% of them (DS: 87.6%; ITS: 88.2%).
Assessment
All assessments were administered on the computer’s touch screen, with structured responses (no open-ended text) consisting of 0–100-point visual analog scales for mood items and single or multiple selections for other domains. The content of the domains is shown in Table 1. (a) Mood ratings of 14 adjectives (listed in Table 1 note) addressing mood, arousal, and attention, respectively, were summarized as four factor scores: Negative Affect, Positive Affect, Arousal, and Attention Disturbance, each analysis of which included linear and quadratic components; (b) Location (if subjects had moved to smoke, they were asked to describe the setting that first prompted them to smoke, otherwise current location was described); (c) Activity; (d) Social Setting; (e) Smoking Setting, i.e. whether others were smoking (and whether they were part of the group of people they were with or were just in view), and whether smoking was restricted; (f) Consumption of Food or Drink in the past 15 min; and, and (g) Time of Day, which was automatically recorded by the palmtop computer. We also assessed craving on a 0–100 scale.
Summary of Stimulus Domains
Analysis
Data set construction is described in detail in Shiffman et al. (2014a). The data set comprised 406 subjects (212 ITS; 194 DS), each contributing an average of 53.02 (SD = 33.00) smoking assessments (ITS: 36.66 [SD = 30.81]; DS: 70.90 [SD = 25.14]) and 66.28 (SD = 19.78) nonsmoking assessments (ITS: 72.07 [SD = 18.32]; DS: 59.94 [SD = 19.42]).
To illustrate the relevance of idiographic analyses, we report the range across subjects of the association between smoking and four illustrative variables: (a) a summary score of emotional state, as captured by a 5-point bipolar item in which subjects indicated how good or bad they were feeling (very bad, bad, neutral, good, very good); (b) a factor score indexing degree of arousal; (c) an indicator of drinking coffee in the previous 15 min (0, 1); and (d) an indicator of being alone (0, 1, where 1 = alone). For each subject, the association of the variables with smoking was estimated by a within-subject correlation coefficient (point-biserial for mood, phi for “coffee” and “alone”). We display the distributions for DS and ITS separately, and also note the standard deviation of the correlations.
Stimulus control was assessed for each situational domain. The analysis proceeded in two steps (Raudenbush & Bryk, 1992; see Shiffman & Paty, 2006), (a) within-subject idiographic analyses performed separately for each subject, and (b) between-groups analyses of the estimated parameters by smoker type. We first assessed the degree to which each participant’s smoking was under stimulus control of the variables in each of several domains of situational context by conducting separate multivariable logistic regressions for each subject to determine how well the situational variables predicted smoking (in contrast to nonsmoking observations). In other words, for each subject and for each domain, we ran a separate logistic regression with smoking (yes, no) as the dependent variable, and the domain variables (see Table 1) as predictors. To account for potential overfitting of models, analyses omitted cases that demonstrated complete or quasi-complete separation (<5% of all cases in each domain). In addition to fitting models for each domain, we also fitted for each subject an omnibus model, including all the variables listed in Table 1. The within-subject logistic models did not take into account the autocorrelation among a subject’s data; the estimates generated are used descriptively. To quantify the degree of prediction (and thus stimulus control) achieved by each of these models, for each subject and domain, we calculated the area under the curve (AUC) for the receiver operating characteristic curve (ROC). Like an R2 value for ordinary regression, higher AUC-ROC values (also sometimes described as the c statistic) indicate better prediction. AUC-ROC is interpretable as the probability of correctly identifying a smoking (vs. nonsmoking) observation given the situational predictors. Thus, AUC-ROC ranges from 0.5 (random guessing) to 1.0 (perfect prediction; Hanley & McNeil, 1982). Accordingly, each subject had an AUC-ROC value for each domain that quantified the degree of “predictability” of smoking from the variables in that domain.
In the second step, to assess whether DS differed from ITS, we tested the between-groups differences (DS vs. ITS) in AUC-ROC for each domain using mixed-regression models (SAS’s PROC MIXED) specifying variance components’ autocorrelation structure. At this second level, each estimate was weighted by the inverse of its standard error (SE), so that more precise estimates received greater weight (Hanley & McNeil, 1982). The SEs of AUC-ROC values decrease as the number of observations increases, and also decrease as the estimated magnitude of the AUC-ROC increases (Hanley & McNeil, 1982). Note that, although DS reported more smoking events, DS and ITS did not differ in average AUC-ROC SEs across any situational domains, though ITS had lower SEs for an omnibus model with all the variables included. Analyses were also weighted by race to account for oversampling of African American participants. To assess whether the AUC-ROCs in each domain differed between ITS and DS, we computed mixed-regression models, treating AUC-ROC values across situational domains as a within-subjects random effect. The analyses also examined whether the DS-ITS differences varied by domain by assessing the interaction between smoker type and situational domain. We used a mixed model to accommodate cases in which subjects had missing estimates for a particular domain (e.g., due to complete separation).
We also report the AUC-ROC for the relationship between craving and smoking, and test whether the group differences in this relationship mediate the group differences in AUC-ROC for each of the stimulus domains. We used the Sobel test (Preacher & Hayes, 2004) to assess the significance of these mediational relationships using separate ordinary least-square regression models, first assessing smoker type as a predictor of craving AUC-ROC (α path) and then examining subjects’ craving AUC-ROC (β path) covarying for smoker type as a predictor of the AUC-ROC for each stimulus domain. The product of the α and β coefficients was used to assess evidence for mediation of stimulus control within each domain (Preacher & Hayes, 2004).
Results Idiographic Variations in Associations of Smoking With Contexts
Figure 1 shows the distribution of within-subject correlations between smoking and several illustrative variables; the correlations each quantify how each variable relates to smoking for each subject. In all cases, the average correlations are near 0, indicating at most a modest association, on average, although some of these associations were significant in analyses reported in Shiffman et al. (2014a). However, this mean value masks the fact that the distributions extend on either side of 0, indicating that there are individuals who show positive associations, as well as others who show negative associations. For example, as seen in Figure 1, for some ITS, being alone was correlated −0.60 with smoking (i.e., they were considerably more likely to smoke with others); for others, being alone was correlated as high as 0.90 with smoking (i.e., they were much more likely to smoke alone). The average correlation among ITS was −0.02, indicating no relationship with smoking, on average (see also Shiffman et al., 2014a). Notably, the spread of the correlations was consistently wider among ITS, as demonstrated by the higher SDs. (This was true of almost all variables, not just those shown in Figure 1.) Furthermore, for all variables, both positive and negative correlations were observed, with the range of subject-specific correlations averaging 1.0 (for example, −0.5 to +0.5 or −0.4 to +0.6).
Figure 1. Correlations between smoking and various situational characteristics, among daily smokers (DS) and intermittent smokers (ITS). The histograms show the range of correlations, at the level of individual subjects, between selected situational characteristics and smoking, shown separately for DS and ITS. The variables shown are (a) feeling/valence, a rating of feelings from negative to positive; (b) arousal, a factor score whose constituent items include “active,” “calm,” “quiet/sleepy,” and “energetic;” (c) drinking coffee (no, yes); and (d) being alone (no, yes). The figures illustrate that the associations vary widely, even when the average association is near 0. Note that the range of the x axes is kept identical for DS and ITS within a given variable to accommodate different ranges of observed correlations, but the scales differ across variables. Each graph also shows the standard deviation of the correlation coefficients shown in the histogram, illustrating that the associations observed among ITS are consistently more variable than those observed among DS.
Stimulus Control of Smoking
When all the situational variables are considered simultaneously, ITS stimulus control was nearly perfect, with AUC-ROC averaging 0.95; that is, smoking and nonsmoking occasions were distinguishable 95% of the time based on the situation descriptors. This was significantly higher than the average AUC-ROC for DS, but it was also very high at 0.86. Figure 2 shows the AUC-ROC values for particular stimulus domains, and shows that both groups demonstrated considerable stimulus control in all domains, with all ROC values significantly higher than the null value of 0.5. However, ITS showed significantly stronger stimulus control over smoking in all stimulus domains. There was also an overall group main effect and ITS values were higher than DS overall and in every domain.
Figure 2. Receiver operating curve (ROC) values across situational domain and smoker group. Average values for the area under the curve (AUC) of the ROC express the predictability of smoking from situational domains. ITS values were significantly higher for every domain, and all values were significantly greater than 0.5, the null value. DS = daily smokers; ITS = intermittent smokers.
To test whether the ITS–DS difference in AUC-ROCs varied by domain, we evaluated the Group × Domain interaction, which was significant (p < .0001). As shown in Figure 2, the differences were greatest for the smoking-context domain and smallest for consumption. Within each smoker group, analyses revealed significant main effects of domain, indicating that some domains are more tightly linked to smoking than others. Within-group differences in AUC-ROC values between domains were nearly all significant. (The exceptions were that, among ITS, AUC-ROC values did not differ between activity and location; among DS, values did not differ between location and smoking context). The AUC-ROC values indicated that social setting, mood, and activity exercised the greatest stimulus control over smoking in both groups. In addition, smoking context was the strongest predictor of ITS smoking; this was not the case for DS.
Mediation of Group Differences by Craving Responsiveness
An AUC-ROC analysis evaluated the relationship between craving and smoking. ITS had a significantly higher value (0.79 vs. 0.63, p < .0001), indicating that their smoking was more closely linked to craving (see also Shiffman et al., 2014a). Covarying the craving AUC-ROC in separate ordinary least squares regression analyses of smoker type effects on AUC-ROCs within each stimulus domain suggested that craving attenuated but did not fully account for ITS and DS group differences in stimulus control (all group differences remained significant at p < .0001). However, tests of mediation suggested that differences in the craving–smoking link partially mediated smoker group effects in nearly all situational domains (Sobel test ps < .01), with the exception of mood (Sobel test p = .12).
DiscussionDetailed data on smoking contexts, collected by real-time EMA methods, demonstrated that situational contexts exercise greater influence over ITS compared with DS smoking. ITS smoking consistently demonstrated significantly greater stimulus control in every situational domain considered: Time of Day, Social Setting, Affect, Restrictions, Location, Activity, and Consumption of Food and Drink. The absolute magnitude of the associations was striking. For example, just knowing the person’s emotional state allowed us to correctly predict, with over 75% accuracy, whether an ITS was smoking or not. In short, ITS smoking seems to be under tight stimulus control.
Even more striking was the estimated level of stimulus control when all variables were considered: the analysis indicated that one could achieve 95% accuracy in identifying smoking situations among ITS. Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations. However, the figures from this omnibus analysis should be treated with some caution, because the models included all 26 variables shown in Table 1, and so may have been overfitted, perhaps achieving spurious levels of prediction.
The finding of stronger stimulus control among ITS across a range of domains is consistent with the hypothesis that stimulus control helps to maintain ITS smoking and make quitting difficult in the face of cues associated with smoking, and may help explain why ITS are not much better able to quit than DS (Tindle & Shiffman, 2011), despite the fact that ITS do not maintain nicotine levels, and do not suffer increased craving or withdrawal when they abstain (Shiffman, Dunbar, Tindle, & Ferguson, 2014). It is also consistent with our previously reported finding that ITS smoking is more responsive to craving than DS smoking (Shiffman et al., 2014b): ITS may experience craving, and hence smoke, when in the presence of certain stimuli, but in the absence of such stimuli, they do not experience a drive to smoke. In a sense, strong stimulus control over use may represent another kind of dependence that keeps users of psychoactive drugs from easily stopping. Given that nondaily use is common for other addictive drugs (SAMHSA, 2003), this mode of dependence may be important for understanding the range of drug-use behaviors.
While the observed degree of stimulus control among ITS was particularly striking, DS smoking also showed a substantial amount of stimulus control—more than would be expected under a strict nicotine-regulation model, which implies smoking at regular intervals, determined by the ebb of nicotine, rather than in response to external stimuli. Further, the pattern of stimulus control across stimulus domains (see Figure 2) was strikingly similar for DS and ITS: Across the seven situational domains examined, the profiles of AUC-ROC values for DS and ITS correlated at 0.90. Thus, stimulus control among DS appears to be qualitatively similar to that in ITS, but consistently weaker.
It is widely understood that even DS smoking is initially under stimulus control during early stages of smoking (Russell, 1971), but the emerging need for nicotine maintenance is thought to supplant stimulus control as a driver of smoking (Shiffman & Paty, 2006). These data suggest that stimulus control remains important even for established adult DS. Perhaps the influence of context is not supplanted, but simply diluted, as smokers begin to smoke more of their cigarettes to maintain nicotine levels above the withdrawal threshold, independent of the situation. In this conceptualization, both ITS and DS respond to similar cues, but, whereas this is the dominant influence on smoking among ITS, its influence on DS is masked by the addition of cigarettes smoked for nicotine maintenance. This account is consistent with the boundary model (Kozlowski & Herman, 1984), which conceptualizes dependence as demanding a certain minimum rate of smoking while allowing for additional smoking that might be prompted by situational influences.
This two-factor model of smoking (Withdrawal Avoidance and Stimulus Control) may also have implications for understanding smoking cessation and relapse among DS, who face a dual challenge when quitting smoking. First, they must overcome withdrawal and background craving (West & Schneiders, 1987), which can be mitigated by pharmacological treatment (Ferguson & Shiffman, 2009). But they also must overcome the influence of stimulus control, which is unmasked during cessation, and triggers cue-elicited craving upon exposure to cues (Ferguson & Shiffman, 2009). The role of stimulus control among DS is evident in lapse situations, which are marked by cueing stimuli like the ones seen in our analyses: for example, exposure to other smokers, consumption of alcohol, and so forth (Bliss, Garvey, Heinold, & Hitchcock, 1989; Shiffman et al., 1997; Shiffman, Paty, Gnys, Kassel, & Hickcox, 1996; Shiffman & Waters, 2004). The re-emergent role of cues also helps explain why smokers relapse (albeit at lower rates) even when their nicotine requirements are met by nicotine replacement. In a study where 100% of baseline nicotine levels were met by a high-dose patch (Shiffman, Ferguson, Gwaltney, Balabanis, & Shadel, 2006) and withdrawal was completely suppressed, 62% of smokers still lapsed within 6 weeks (vs. 75% on placebo; Shiffman et al., 2006) when cued by the typical situational triggers (Ferguson & Shiffman, 2010, 2014). Thus, two factors appear to maintain smoking and make quitting difficult for DS: the need to maintain nicotine levels to avoid withdrawal and abstinence-induced craving, and the influence of cues that trigger cue-induced craving and smoking (i.e., stimulus control).
Previous analyses (Shiffman et al., 2014b) showed that ITS smoking was more tightly linked to craving, because ITS reported very little craving when they were not smoking. Analyses in the present paper showed more broadly that ITS smoking was more sensitive to situational craving, but mediational analyses showed that this did not account for the difference between stimulus control in ITS and DS smoking. The actual elicitation of smoking by situational stimuli may still be due to their stimulation of craving; the analysis only suggests that once craving is elicited, differential responsiveness to that craving does not explain differences in stimulus control.
The idiographic n = 1 analyses used here revealed patterns not seen in group-wise nomothetic analyses. It was particularly striking that nomothetic analyses showed almost no relationship between emotional state and smoking among DS, either in this study or in others (Shiffman et al., 2014a; Shiffman et al., 2002; Shiftman, Paty, Gwaltney, & Dang, 2004), and, consistent with this, Figure 1 shows little or no relationship between emotional state and smoking, on average. Yet, considered idiographically, emotional state was among the most important situational influences on DS and ITS smoking, suggesting that emotion does influence smoking, but not in a simple, consistent way. Important to note, the observed influence of affect on smoking is not readily attributable to withdrawal effects, because it includes cases in which smoking was associated with positive emotional states. Indeed, in traditional analyses of the role of affect in smoking, smoking was more likely to occur when subjects—both ITS and DS—were feeling better, rather than worse (Shiffman et al., 2014a).
The study’s limitations include reliance on self-report of smoking status and situational characteristics, potential for reactivity, and possible biasing effects of noncompliance and smoking restrictions (see Shiffman, 2009a). Particularly when there were few smoking observations, the individual logistic regressions could have exploited chance relationships; this was particularly the case for the omnibus models, as they included many predictors. Also, differences in AUC-ROC values across domains could have been due to differences in how domains were assessed, rather than true differences in their influence on smoking. Some stimulus domains may not have been covered as comprehensively or assessed as reliably as others, perhaps resulting in lower average AUC-ROCs due to these measurement factors. Finally, unlike traditional animal studies of stimulus control, we did not control the pairing of specific antecedent stimuli and our target behavior (smoking) and as such, we cannot draw causal conclusions about the associations observed; that is, although the patterns observed are consistent with stimulus control, we cannot conclude that they are caused by it.
The study’s strengths included the use of real-time EMA methods, and a nontreatment-seeking sample with diverse smoking behavior. An important aspect of our work here was the ability to expand the scope of the analysis from unidirectional and univariate nomothetic relationships that were similar across subjects (e.g., all subjects tending to smoke when feeling worse emotionally) to encompass the fact that different individuals have different, indeed opposite, associations (e.g., some subjects smoke when feeling worse emotionally, and some smoke when feeling better; Figure 1). The analysis by domains, which encompassed several related situational characteristics, also allowed variation across subjects in whom particular variables were influential. For example, if some subjects tended to smoke more when drinking alcohol, and others to smoke more when drinking coffee, such effects might be diluted, perhaps to the point of being invisible, in traditional analyses treating alcohol and coffee as separate cues. In contrast, both effects would have been included in our analysis of the stimulus control exerted by consumption. Particularly because such heterogeneity in influential variables, and in their direction of influence, is to be expected if these individual differences resulted from idiosyncratic learning histories (Niaura et al., 1988), this mode of analysis seems important for assessing the influence of situational variables on smoking, that is, stimulus control.
In summary, ITS demonstrated very strong stimulus control over smoking, which may be a dominant driver of their smoking, and may account for their surprising difficulty quitting. DS also showed substantial stimulus control, suggesting that stimulus control also plays a significant role in driving and maintaining smoking even among DS. DS smoking may be maintained by two factors—withdrawal avoidance and stimulus control—whereas ITS smoking may be maintained primarily by stimulus control.
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Submitted: September 5, 2014 Revised: November 13, 2014 Accepted: November 23, 2014
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 847-855)
Accession Number: 2015-07274-001
Digital Object Identifier: 10.1037/adb0000052
Record: 59- 'Stimulus control in intermittent and daily smokers': Correction to Shiffman, Dunbar, and Ferguson (2015).No authorship indicated; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 855. Publisher: American Psychological Association; [Erratum/Correction] Abstract: Reports an error in 'Stimulus Control in Intermittent and Daily Smokers' by Saul Shiffman, Michael S. Dunbar and Stuart G. Ferguson (Psychology of Addictive Behaviors, Advanced Online Publication, Feb 23, 2015, np). There was an error in the reported value in the Discussion section. The second sentence of the second paragraph should have read, 'Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations.' This was a result of a transcription error. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2015-07274-001.) Many adult smokers are intermittent smokers (ITS) who do not smoke daily. Prior analyses have suggested that, compared with daily smokers (DS), ITS smoking was, on average, more linked to particular situations, such as alcohol consumption. However, such particular associations assessed in common across subjects may underestimate stimulus control over smoking, which may vary across persons, due to different conditioning histories. We quantify such idiographic stimulus control using separate multivariable logistic regressions for each subject to estimate how well the subject’s smoking could be predicted from a panel of situational characteristics, without requiring that other subjects respond to the same stimuli. Subjects were 212 ITS (smoking 4–27 days/month) and 194 DS (5–30 cigarettes daily). Using ecological momentary assessment, subjects monitored situational antecedents of smoking for 3 weeks, recording each cigarette in an electronic diary. Situational characteristics were assessed in a random subset of smoking occasions (n = 21,539), and contrasted with assessments of nonsmoking occasions (n = 26,930) obtained by beeping subjects at random. ITS showed significantly stronger stimulus control than DS across all context domains: mood, location, activity, social setting, consumption, smoking context, and time of day. Mood and smoking context showed the strongest influence on ITS smoking; food and alcohol consumption had the least influence. ITS smoking was under very strong stimulus control; significantly more so than DS, but DS smoking also showed considerable stimulus control. Stimulus control may be an important influence on maintaining smoking and making quitting difficult for all smokers, but especially among ITS. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Correction to Shiffman, Dunbar, and Ferguson (2015)
In the article “Stimulus Control in Intermittent and Daily Smokers” by Saul Shiffman, Michael S. Dunbar, and Stuart G. Ferguson (Psychology of Addictive Behaviors, Advance online publication. February 23, 2015. http://dx.doi.org.offcampus.lib.washington.edu/10.1037/adb0000052), there was an error in the reported value in the Discussion section. The second sentence of the second paragraph should have read, “Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations.” This was a result of a transcription error. All versions of this article have been corrected.
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 855)
Accession Number: 2015-33590-001
Digital Object Identifier: 10.1037/adb0000105
Record: 60- Symptoms of posttraumatic stress predict craving among alcohol treatment seekers: Results of a daily monitoring study. Simpson, Tracy L.; Stappenbeck, Cynthia A.; Varra, Alethea A.; Moore, Sally A.; Kaysen, Debra; Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012 pp. 724-733. Publisher: American Psychological Association; [Journal Article] Abstract: Alcohol use disorders (AUDs) and Posttraumatic Stress Disorder (PTSD) commonly co-occur. Craving for alcohol is a common aspect of AUD, with and without PTSD, and is one of the key predictors of continued problematic alcohol use among treatment seekers. The present study sought to investigate the self-medication hypothesis using daily Interactive Voice Response (IVR) reports to examine the relationships between PTSD symptomatology and both same-day and next-day alcohol craving. Twenty-nine individuals with an AUD (26 of whom screened positive for PTSD) entering AUD treatment provided daily IVR data for up to 28 days regarding their alcohol use, craving, and 7 symptoms of PTSD. Given the nested nature of daily data, generalized estimating equations using a negative binomial distribution and a log link function were used to test hypotheses. Results suggest that days with greater overall PTSD severity are associated with greater alcohol craving, and greater reports of startle and anger/irritability were particularly associated with same-day craving. The next-day results suggest that the combination of the 7 PTSD symptoms did not predict next-day craving. However, greater distress from nightmares the previous night, emotional numbing, and hypervigilance predicted greater next-day craving, while greater anger/irritability predicted lower next-day craving. These findings highlight the importance of assessing the relationship between specific symptoms of PTSD and alcohol cravings in order to increase our understanding of the functional interplay among them for theory building. Additionally, clinicians may be better able to refine treatment decisions to more efficiently break the cycle between PTSD-related distress and AUD symptoms. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Symptoms of Posttraumatic Stress Predict Craving Among Alcohol Treatment Seekers: Results of a Daily Monitoring Study
By: Tracy L. Simpson
VA Puget Sound Health Care System, Seattle, Washington;
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine;
Cynthia A. Stappenbeck
VA Puget Sound Health Care System, Seattle, Washington
Alethea A. Varra
VA Puget Sound Health Care System, Seattle, Washington;
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine
Sally A. Moore
Evidence Based Treatment Centers of Seattle, PLLC, Seattle, Washington;
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine
Debra Kaysen
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine
Acknowledgement: Cynthia Stappenbeck is currently a postdoctoral fellow at the Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine.
Funding for this study was provided by pilot grants from the VA Puget Sound Health Care System Mental Illness Research, Education, and Clinical Center (MIRECC) and from the University of Washington Alcohol and Drug Abuse Institute.
Alcohol use disorders (AUDs) are among the most common psychiatric disorders, with approximately 14% of the general population having a lifetime history of alcohol dependence (AD; Kessler et al., 1997). AUDs have a chronic and relapsing course (Brownell, Marlatt, Litchenstein, & Wilson, 1986) and are associated with high personal and societal costs (McGinnis & Foege, 1993; Rice, Kelman, & Miller, 1991). One of the key predictors of continued problematic alcohol use among individuals seeking treatment is craving for alcohol. Craving predicts relapse among abstinent alcoholics (Bottlender & Soyka, 2004; Sinha et al., 2011) as well as the amount of alcohol consumed during treatment (Flannery, Poole, Gallop, & Volpicelli, 2003).
AUDs often present with comorbid disorders (Kessler et al., 1997), including posttraumatic stress disorder (PTSD; Chilcoat & Breslau, 1998; Dansky et al., 1996; McFarlane, 1998). Co-occurring PTSD/AUD is typically associated with worse functioning, greater health care utilization, poorer treatment response, higher dropout, and faster alcohol relapse (Ouimette, Moos, & Finney, 2000; Read, Brown, & Kahler, 2004). Although there is some discrepancy in the extant literature, specific PTSD symptom clusters have been found to be most associated with alcohol problems, including emotional numbing, hyperarousal, and reexperiencing symptoms (Jakupcak et al., 2010; Maguen, Stalnaker, McCaslin, & Litz, 2009; McFall, Mackay, & Donovan, 1992), essentially all aspects of the PTSD diagnosis but behavioral avoidance.
The self-medication theory (Khantzian, 2003) has been posited to explain the frequent co-occurrence of AUDs and PTSD. The general theory asserts that distress associated with symptoms of PTSD results in drinking alcohol to reduce the discomfort associated with these symptoms via negative reinforcement, which then increases the likelihood that the person will use alcohol again in the future to manage PTSD-related distress. Over time, this is thought to lead to the development of an AUD (Chilcoat & Breslau, 1998; Simpson, 2003; Stewart, Conrod, Pihl, & Dongier, 1999). A somewhat more general formulation of this basic idea is the stress–response dampening (SRD) theory, which posits that alcohol may be used to mitigate negative reactions to stress, which, for those with PTSD, could include stressful situations, trauma memories, or PTSD symptoms (see Armeli et al., 2003, for an in-depth discussion of SRD). Moreover, some individuals may drink to dampen or disrupt their self-awareness, narrow their attention, or change their appraisals of stressful situations to relieve discomfort associated with stressors (Sayette, 1993). This specification of hypothesized mechanisms behind the use of alcohol to manage symptoms is known as the attention disruption model. Although some support for this model has been found among moderate to heavy drinkers (Armeli et al., 2003), to our knowledge, it has not been evaluated in PTSD/SUD samples. The self-medication hypothesis is more narrowly focused than the SRD model in its emphasis on symptom relief, as opposed to stress more generally, and it is not as nuanced as the attention disruption model, as it does not specify potential mechanisms of the posited effect (i.e., that relief from distress is due to alcohol's effect of disrupting attention, self-awareness, or changing appraisals). Nonetheless, as a working model through which to evaluate whether there appears to be a reliable relationship between PTSD symptomatology and drinking behaviors, the self-medication model is worthy of evaluation.
Trauma exposure and PTSD have been found to precede AUD development (Stewart et al., 1999; Volpicelli, Balaraman, Hahn, Wallace, & Bux, 1999), suggesting that alcohol use may be a learned coping response. However, data also show that alcohol and other drug abuse or dependence may increase the risk of traumatization (Acierno, Resnick, Kilpatrick, Saunders, & Best, 1999; Chilcoat & Breslau, 1998; Messman-Moore, Ward, & Brown, 2009) as well as impede natural recovery from trauma, thereby increasing the risk of developing chronic PTSD (Kaysen et al., 2006, (2011a); McFarlane et al., 2009). These phenomena suggest that there is perhaps a reciprocal or mutual maintenance relationship between the two. Cross-sectional studies of individuals with AUD with and without PTSD have found that, compared with those with only AUD, those with comorbid AUD/PTSD are more likely to report being motivated to drink to cope with negative affect and stress (Grayson & Nolen-Hoeksema, 2005; Kaysen et al., 2007; Ullman, Filipas, Townsend, & Starzynski, 2005), further suggesting that there is likely a functional relationship between PTSD symptomatology and drinking behavior regardless of the temporal order of onset.
As a negative reinforcement model such as the self-medication theory would predict, alcohol cravings are often triggered by negative emotional cues (Cooney, Litt, Morse, Bauer, & Gaupp, 1997; Rubonis, Colby, Monti, & Rohsenow, 1994) and, specifically, by trauma-related cues and distress for individuals with PTSD (Coffey et al., 2002; Saladin et al., 2003). A laboratory-based study involving cue-exposure paradigms found that individuals with comorbid PTSD and AUD experience increases in craving for alcohol in response to both trauma-related and alcohol cues relative to neutral cues (Coffey et al., 2002). However, a later study using similar methodology found that individuals with comorbid AUD and PTSD experience increased craving in response to trauma cues with or without exposure to an alcohol cue (Coffey et al., 2010). Similarly, baseline PTSD severity has been found to be associated with how much craving is elicited by trauma and alcohol cues, with those reporting more severe baseline PTSD also reporting stronger craving responses (Saladin et al., 2003). Although there is emerging evidence of a reciprocal relationship between alcohol use and PTSD (Kaysen et al., in press), it is not known whether craving for alcohol is linked to subsequent PTSD symptom exacerbation. It is possible that discomfort associated with craving is activating and may lead to worsened PTSD.
Research on the relationship between PTSD symptoms and alcohol craving has typically relied on either laboratory-based alcohol cue and imaginal paradigms such as the ones described here or on retrospective reconstruction of how these phenomena interact with one another in individuals' day-to-day lives (Ouimette, Read, Wade, & Tirone, 2010). While laboratory-craving paradigms help to establish what does and does not elicit craving for different groups of drinkers, they are necessarily limited with regard to ecological validity. Research that relies on retrospective symptom assessment makes it difficult to evaluate the interrelationships between PTSD and alcohol use because these assessments are likely biased by recall errors (McKay, 1999; Simpson et al., 2011). Neither approach can capture the interplay between craving and PTSD or other symptoms as it unfolds over time. For example, simple pre- and posttest assessments cannot identify whether an exacerbation of PTSD symptoms is associated with increased alcohol craving or use on a given day. Although laboratory-based studies utilizing craving inductions have been helpful in examining this issue (Coffey et al., 2002; Saladin et al., 2003), these studies, by their very nature, are artificial and still cannot address how PTSD may affect alcohol cravings in people's day-to-day lives.
In order to better assess these clinically and conceptually important issues, researchers have increasingly begun to utilize daily monitoring of symptoms to examine predictors of substance craving, use, and relapse (Armeli, Conner, Cullum, & Tennen, 2010; Collins et al., 1998; Lukasiewicz, Benyamina, Reynaud, & Falissard, 2005; Shiffman et al., 2007; Warthen & Tiffany, 2009). Such assessments allow for close to real-time examination of temporally ordered event-level relationships and thus can answer questions regarding the daily interrelationships among symptoms, and are less subject to recall errors and bias than traditional retrospective methods (Galloway, Didier, Garrison, & Mendelson, 2009; Leigh, 2000). For example, retrospective reports of smoking lapses are affected by attempts to justify lapses, such as attributing them to stress, whereas prospective analyses of daily data fail to find this relationship (Shiffman & Waters, 2004).
Because of their advantages, daily monitoring protocols are being applied to studies on the relationship between alcohol craving and other phenomena such as relapse, negative affect, and coping. For example, in a study of college students participating in a 12-step oriented recovery program, daily reports of alcohol craving were associated with negative affect and negative social experiences, moderated by avoidance coping (Cleveland & Harris, 2010). In alcohol-dependent individuals involved in residential treatment, latent class analyses revealed that consistently high daily reports of craving were associated with less time to relapse following treatment (Oslin, Cary, Slaymaker, Colleran, & Blow, 2009). These studies highlight the likely utility of capturing daily reports of craving and PTSD symptom severity to further our understanding of the interrelationships between them.
The present study is a preliminary investigation of the self-medication hypothesis in a sample of individuals beginning a new episode of treatment for an AUD, most of whom screened positive for PTSD. The data are from those who were randomly assigned to a daily monitoring condition as part of a larger study that evaluated the feasibility of utilizing 28 days of Interactive Voice Response (IVR) system to monitor alcohol use and cravings, as well as PTSD symptoms, with individuals seeking alcohol treatment (Simpson, Kivlahan, Bush, & McFall, 2005). To our knowledge, this methodology has not been used to examine the interrelations between alcohol craving and PTSD symptom severity among AUD-treatment-seeking individuals. Based on the self-medication and negative reinforcement models, we hypothesized that PTSD symptoms would be positively associated with same-day and next-day alcohol craving, such that higher reports of PTSD symptoms would be associated with greater alcohol craving the same day and the next day. We also evaluated whether there was a predictive relationship between craving and later PTSD symptoms to examine a mutual maintenance pattern, wherein increases in cravings predict increases in same-day PTSD symptom severity. In the current study, alcohol use itself was not examined, due to the low rates of drinking reported during the daily monitoring interval.
Method Participants
Thirty-six individuals with a current alcohol use disorder (American Psychiatric Association, 1994) and who experienced drinking in the past month were randomized to the daily monitoring condition of the larger study (Simpson et al., 2005). Of those, 29 (80% of the original daily monitoring sample) provided information for at least 50% of the 28-day monitoring period and are the focus of this report. They were recruited from either a large VA medical center (n = 24) or a large, urban publicly funded community addiction treatment program in Seattle, Washington (n = 5). The mean age of this sample was 48.0 years (SD = 7.0 years) and 93% were male. The self-identified ethnic composition of the sample was as follows: 41% African American, 10% Native American, 45% non-Hispanic White, and 4% Other. Over half of the sample was currently separated, divorced, or widowed (79.4%), 17.2% were single, and 3.4% were married or partnered. Just under half of the participants lived in their own homes (44.8%), 44.8% were homeless, and 10.3% were in other living situations (i.e., VA domiciliary, assisted care living). Over half of the participants had attended at least some college (62.1%), 24.1% had a high school degree or the equivalent, and 13.8% had not completed high school. The majority of the participants were unemployed (72.4%).
The sample was, on average, in the severe range on the AUD Identification Test (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001; M = 26.2, SD = 8.3, range = 0–40). Additionally, all of the participants experienced at least three of the 16 potentially traumatic events (M = 9.8, SD = 3.6, range = 3 – 16) listed in the Life Events Checklist (LEC). Criterion A1 and A2 (i.e., whether actual or threatened harm or violation of physical integrity occurred, and whether response involving fear, helplessness, or horror occurred at the time, respectively) were not assessed. Based on the PTSD Checklist – Civilian version (PCL-C; Blanchard, Jones-Alexander, Buckley, & Forneris, 1996), 89.7% of the sample screened positive for current PTSD at intake, with a score of 38 or higher for women (Dobie et al., 2002) and 42 or higher for men (Spiro, Hankin, Leonard, & Stylianou, 2000). In addition, when the PCL-C was scored to reflect the Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV; American Psychiatric Association, 1994) criteria regarding the required number of symptoms endorsed in each symptom cluster, the results were the same, except for one person who scored a 50 but missed the hyperarousal criteria by 1 point.
Procedure
Eligible participants provided informed consent as approved by the University of Washington Human Subjects Division Internal Review Board. Consenting participants completed a detailed interview regarding their recent alcohol and drug use, treatment utilization, and legal involvement, as well as several paper-and-pencil measures. They then received instruction regarding the IVR system and completed a practice call. Participants were paid $25.00 for the baseline and $25.00 for the follow-up assessments.
Compliance with the monitoring protocol was automatically tracked by the IVR system. When participants failed to call the system as scheduled, the study coordinator attempted to contact participants within 2 working days in order to reconstruct the data from missed calls verbally and to answer any questions about the IVR system.
Monitoring incentives
We used the same payment schedule as Searles, Helzer, Rose, and Badger (2002). Participants received $0.50 per call, and if they made all seven required calls in a week, they received a bonus of $10.00. If a participant missed only 2 nonconsecutive days over the entire 28-day study period, they then received a prorated bonus of $7 for weeks with any missing calls. Participants could earn up to $54 for perfect compliance over the 28 days of monitoring.
Measures
AUD status
Current and lifetime DSM–IV AUD status was assessed via the interview version of the Structured Clinical Interview for DSM–IV (SCID; First, Spitzer, Gibbon, & Williams, 1996). The SCID is a widely used structured interview that assesses Axis I psychiatric history. The SCID-I has been shown to have very good reliability (Zanarini et al., 2000) and validity (Kranzler et al., 2003).
Alcohol severity
The Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) is a widely used 10-item screen for evaluating the severity of alcohol misuse during the previous year. The internal consistency of the scale has been found to be good (Cronbach's alpha ≥ .85; Babor et al., 2001).
Alcohol craving
The Penn Alcohol Craving Scale (PACS; Flannery, Volpicelli, & Pettinati, 1999) is a 5-item scale developed to assess various aspects of craving for alcohol over the past week. It was administered at baseline and follow-up. The internal consistency of the PACS was high in the current sample (Cronbach's alpha = .93).
Trauma exposure
The LEC (Blake et al., 1995) consists of a list of 16 potentially traumatic events from the Clinician-Administered PTSD Scale (CAPS; Blake et al., 1995). Respondents indicated whether each event happened to them, they witnessed it, or they learned about the event. For the present study, events were included in our assessment of trauma exposure that either happened to the individual or were witnessed by him or her.
PTSD symptomatology
The PCL-C (Blanchard et al., 1996) is a 17-item questionnaire that directly parallels the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) diagnostic criteria for PTSD. Participants rated how much they were bothered in the past month by each symptom on a 5-point scale ranging from “not at all” to “extremely.” The PCL-C was found to be highly correlated with CAPS, the “gold standard” diagnostic interview of PTSD (r = .93), and to have adequate internal reliability (Blanchard et al., 1996). Moreover, the PCL screening appears to have good specificity and sensitivity in identifying PTSD cases compared with other PTSD measures across trauma-exposed populations (Lang, Laffaye, Satz, Dresselhaus, & Stein, 2003; Ruggiero, Del Ben, Scotti, & Rabalais, 2003).
Daily monitoring protocol
Participants called a prerecorded IVR system using a toll-free telephone number. The 25-item daily monitoring protocol was adapted from the work of Searles, Helzer, & Walter (2000). To assess PTSD symptomatology, we selected seven items from the PCL-C (upsetting dreams, upset due to reminders, avoiding reminders, emotionally numb, hypervigilance, increased startle response, anger/irritability). This limited item set was used to minimize participant burden. The items were chosen by a panel of PTSD experts so as to include symptoms that were likely to vary from day to day and that had been shown in cross-sectional research to be associated with alcohol use (McFall et al., 1992). The PCL-C wording was modified to fit the daily time frame, and the phrase “stressful experience” was replaced with “traumatic experience” to increase the likelihood that the focus was on experiences that fit Criterion A of the PTSD diagnostic requirements. Craving was assessed with the following question, adapted from the PACS, to reflect the daily assessment (Item #2): “At its most severe point, how strong was your craving for alcohol yesterday, with 0 being not at all strong and 7 being very strong?” Daily responses to the craving item were significantly correlated with the PACS assessed at baseline, r = .57, p < .001, and follow-up, r = .70, p < .001. The IVR calls lasted, on average, 3.8 minutes the first week (SD = 1.3) and were down to 2.6 minutes by Week 4 (SD = 1.3).
Data Analytic Approach
The daily alcohol craving data were nested within individuals and positively skewed. Therefore, generalized estimating equations (GEE; Hardin & Hilbe, 2003), using a negative binomial distribution and a log link function, were used to test hypotheses about the daily relations between PTSD symptom severity and craving for alcohol. GEE models provide an alternative approach to modeling multilevel data when the response variables are non-normally distributed. GEEs with a negative binomial distribution permit the use of incidence rate ratios (IRRs) as a measure of effect size. GEE models are useful for handling cases with missing observations. Whereas many other analyses (e.g., repeated measures ANOVAs) would exclude participants with incomplete data, GEE modeling allows participants with missing data to be included in the analyses. Four separate GEE models were run to examine the influence of PTSD symptoms on same-day and next-day alcohol craving: (a) seven combined PTSD symptoms predicting alcohol craving on the same day; (b) each of the seven PTSD symptoms entered individually predicting alcohol craving on the same day; (c) combined symptoms predicting next-day craving, and (d) each symptom entered individually predicting next-day craving. Eight GEE models were run to examine the influence of craving on next-day PTSD symptoms: (a) alcohol craving predicting next-day overall PTSD symptom severity, and (b) alcohol craving predicting next-day individual PTSD symptoms separately. In all of these models, we included indicator variables for each day of the week, with Sunday as the reference day, to control for day-to-day variation in PTSD symptoms and craving for alcohol. We also included a monitoring-day variable to test and control for reactivity effects in the endorsement of craving for alcohol. All analyses were conducted in Stata 10.1 (StataCorp, 2009).
Results Preliminary Analyses
Chi-square analyses were conducted to examine gender and race/ethnicity differences between participants included versus participants excluded from the analyses. There were no differences between those included or excluded from analyses on gender, χ2(1, N = 36) = 0.51, p = .48, or race/ethnicity, χ2(1, N = 36) = 0.99, p = .80. Univariate analysis of variances (ANOVAs) were conducted to examine differences on baseline alcohol use, alcohol craving, and PTSD symptom severity between those included versus excluded from analyses. There were no differences between those included (M = 26.17, SD = 8.32) and excluded (M = 28.00, SD = 10.34) on overall alcohol use, F(1, 34) = 0.25, p = .62. There were, however, differences between the groups on baseline alcohol craving, F(1, 34) = 7.14, p = .01, and PTSD symptoms, F(1, 33) = 5.19, p = .03. Individuals included in the analyses reported lower baseline craving for alcohol (M = 3.45, SD = 1.59) than those excluded (M = 5.29, SD = 1.80), and higher baseline PTSD symptoms (M = 60.04, SD = 13.96) than those excluded (M = 46.71, SD = 13.26) from analyses.
The aggregate summary statistics regarding PTSD symptom severity and alcohol craving for the daily monitoring period are shown in Table 1, along with information about compliance with the IVR system. Among these participants, baseline retrospective reports of PTSD severity (PCL-C scores) were significantly correlated with baseline reports of alcohol craving (PACS scores), r(n = 29) = .393, p = .04, and baseline reports of days drinking (r = .17, p < .001). In order to examine the extent of actual drinking behavior reported during the monitoring period, we reviewed summary statistics regarding the number of days drinking and the number of drinks per drinking day. Among the 29 participants included in the final sample, participants monitored for an average of 25.5 days (SD = 3.03), and data were provided for 741 out of a possible 812 days (91.3%). Alcohol was consumed on 85 days (11.5%). On those drinking days, participants consumed between 1 and 24 drinks, with an average of 5.9 drinks (SD = 3.4) and a mode of 8 drinks. In this sample, daily reports of alcohol use were significantly correlated with daily reports of craving, r = .34, p < .001. Due to the low base rate of drinking days, however, alcohol use itself was not examined.
Aggregate Summary Statistics of PTSD Symptom Severity and Alcohol Craving During the Daily Monitoring Period
PTSD Symptom Severity Predicting Same-Day Alcohol Craving
As shown in Table 2, the GEE model examining the influence of overall combined PTSD severity on same-day alcohol craving found that higher overall PTSD severity was associated with greater craving for alcohol on the same day, b = 0.03, IRR = 1.03, p < .001. Additionally, alcohol craving was higher among women, b = 0.79, IRR = 2.20, p < .05, and on Saturday compared with Sunday, b = 0.21, IRR = 1.23, p < .05. A second GEE model investigating the same-day relations between the severity of the individual PTSD symptoms and alcohol craving found that greater daily reports of startle response, b = 0.10, IRR = 1.10, p < .01, and anger/irritability, b = 0.08, IRR = 1.08, p < .01, were associated with greater craving for alcohol on the same day. Alcohol craving was also higher among women, b = 0.69, IRR = 1.99, p < .05, and on Saturday compared with Sunday, b = 0.21, IRR = 1.23, p < .05.
Overall PTSD Severity and Individual PTSD Symptom Severity Predicting Same-Day Alcohol Craving
PTSD Symptom Severity Predicting Next-Day Alcohol Craving
In the model examining overall PTSD severity and next-day alcohol craving, women reported greater craving for alcohol than men, b = 1.11, IRR = 3.03, p < .01 (see Table 3). Contrary to our hypotheses, overall PTSD symptom severity was not significantly associated with next-day alcohol craving, b = 0.01, IRR = 1.01, p = .11. A different pattern emerged when the PTSD symptoms were examined individually. Greater reports of upsetting dreams, b = 0.08, IRR = 1.03, p < .01, emotional numbing, b = 0.06, IRR = 1.06, p < .05, and hypervigilance, b = 0.07, IRR = 1.07, p < .05, were associated with greater next-day craving for alcohol, whereas greater reports of anger/irritability were associated with lower next-day craving for alcohol, b = −0.06, IRR = 0.94, p < .05.
Overall PTSD Severity and Individual PTSD Symptom Severity Predicting Next-Day Alcohol Craving
Alcohol Craving Predicting Next-Day PTSD Symptom Severity
In order to evaluate whether there might be a reciprocal relationship between craving and PTSD symptomatology, and to better insure that the pattern of results indicating that specific PTSD symptoms predict next-day craving was not spurious, GEE models were also conducted to determine whether craving severity predicted next-day PTSD symptom severity. Neither the overall model involving craving predicting the overall PTSD symptom severity nor the one involving craving predicting each PTSD symptom separately was significant (all ps > .21).
DiscussionThe present study is a preliminary investigation of the day-to-day relationships between posttraumatic symptomatology and alcohol craving among individuals seeking treatment for an AUD, most of whom screened positive for comorbid PTSD. The within-day pattern of results suggests that on days with greater PTSD severity, these individuals also experienced greater craving that same day. PTSD symptoms also predicted next-day craving, which would generally support the self-medication hypothesis, as it suggests that individuals may respond to the emotional distress of PTSD symptom exacerbations with increased urges to use alcohol. Of the symptoms of PTSD that we assessed via the daily IVR monitoring protocol, increased startle and increased anger/irritability were particularly associated with greater craving on a given day. These symptoms are from the hyperarousal cluster of the DSM–IV PTSD diagnostic criteria (American Psychiatric Association, 1994). Although preliminary, due to the small sample size and the restricted symptom pool included in the IVR protocol, these results suggest that the experience of heightened psychophysiological arousal on a given day may be especially problematic, leading to increased alcohol craving. Although we were not able to examine consumption itself, due to the low rate of drinking in this sample of individuals in early recovery, it is possible that this may also lead to increased risk for alcohol consumption and relapse. This pattern of results is consistent with the earlier cross-sectional study by McFall and colleagues (1992), which found that the hyperarousal cluster was the only PTSD symptom cluster significantly associated with alcohol use among Vietnam veterans, although other studies have failed to find a relationship between hyperarousal specifically and drinking (Jakupcak et al., 2010; Maguen et al., 2009; Read et al., 2004). Inconsistencies across these studies may reflect differences in gender of the participants, recency and severity of trauma exposure, treatment-seeking status, and whether the sample was still drinking. Studies of alcohol craving have generally not disaggregated PTSD symptoms, but further studies may wish to elucidate which PTSD symptom changes are most indicative of increased cravings.
The effects of PTSD symptoms on next-day alcohol craving were substantially different from the same-day pattern of results. Specifically, following a day of relatively higher PTSD symptoms, overall symptom severity was not associated with higher alcohol craving. However, on the individual symptom level, more distress from nightmares the night before, more emotional numbing, and higher hypervigilance predicted greater alcohol craving on the next day. Both reexperiencing and hyperarousal symptoms, when measured as symptom clusters, have been associated with increased alcohol use (Maguen et al., 2009; Read et al., 2004), as has emotional numbing (Jakupcak et al., 2010). As all of the research conducted to date has consisted of retrospective data at the between-persons, rather than within-persons, level, these findings suggest that PTSD symptoms and alcohol symptoms may influence each other in complex ways across days. If these findings are replicated and extended to include actual drinking behavior, especially in an acute-trauma-exposed sample, they may further elucidate how PTSD and alcohol use and cravings gradually develop into comorbid presentations over time.
The present results also suggest that greater anger/irritability the day before was associated with lower craving. It is puzzling that greater anger was associated with greater craving within the same day but was associated with lower craving the next day. While we have no ready explanation for this finding, it is possible that, for some participants, this shift was due to having consumed alcohol the same day anger was high, thereby perhaps temporarily mitigating craving the next day. The limited sample size and the generally low rates of alcohol consumption did not allow this possibility to be tested, but it is an important finding to follow up on in future research.
It is also noteworthy that we found no support for the idea that alcohol craving leads to next-day PTSD symptom exacerbation. The overall pattern of findings that greater PTSD symptomatology on a given day is associated with increased craving the next day, but that craving does not appear to be related to next-day PTSD symptom severity, lends more support to the self-medication model than to a mutual maintenance model. However, our inability to test these relationships using actual drinking behavior is unfortunate and tempers any conclusions that may be drawn. Future studies should attempt to examine the relationship between alcohol consumption and PTSD, especially in regard to the possibility of mutual maintenance of symptoms.
While the current study has noteworthy strengths, including the use of GEE to analyze data from an extended daily longitudinal assessment protocol with alcohol-treatment-seeking individuals who complied reasonably well, there are also important methodological limitations. The sample size is small, consists largely of males, and is mostly comprised of veterans. Small numbers of participants make it more likely that findings reflect vagaries of the sample. When looking at subgroups, like women, it is even more important to consider issues regarding a small and nonrepresentative sample. We also did not assess actual PTSD diagnostic status but instead screened for likely PTSD with the commonly used PCL-C. While the PCL-C has been found to be highly correlated with the gold-standard CAPS, caution should be exercised in generalizing the present results to individuals with comorbid PTSD/AUD. In order to follow-up on the findings regarding specific PTSD symptoms and findings regarding differential effects of gender, it is necessary to use event level methodologies with larger sample sizes, with a greater proportion of women, and with civilian samples whose PTSD diagnostic status is established with standard diagnostic assessments.
An additional limitation is that the IVR monitoring protocol included only 7 of the 17 standard PTSD symptoms laid out in the DSM–IV. This decision was carefully considered to reduce measurement burden and because there were no other studies involving treatment-seeking individuals at the time this study was initiated. Based on the novelty of the methodology with this population, we determined that the risk of overburdening participants, causing possible distress, or encouraging nonresponses and increasing missing data were greater than the benefit of attempting to assess all 17 PTSD symptoms. However, this choice does significantly limit the interpretability of the results. Future studies should examine the influence of all 17 PTSD symptoms using a daily assessment protocol for people with comorbid PTSD and AUD to test which PTSD symptoms are associated with same-day and next-day alcohol craving and use. Studies with more participants and a more comprehensive daily measure of PTSD could also be used for survival analyses to examine whether PTSD and increased cravings lead to alcohol lapses. Moreover, our use of the PCL items also does not link the symptoms to particular Criterion A stressors. Thus, it is possible that respondents were endorsing symptoms that were more indicative of depression or of more general distress than PTSD per se. Replication of this study in combination with a diagnostic measure of PTSD would help to address this issue.
Despite these important limitations, these findings have potential clinical implications. Our findings regarding specificity of PTSD symptoms and alcohol cravings highlight the need to assess symptoms of PTSD that may contribute to craving for, and subsequent use of, alcohol rather than simply tracking a client's overall level of PTSD severity. The present results suggest that individuals with comorbid PTSD and an AUD could particularly benefit from interventions that would either ameliorate PTSD symptoms or improve their ability to cope with these symptoms, so as to reduce the risk of craving and relapse. For example, Hien, Cohen, Miele, Litt, and Capstick (2004) demonstrated that two behavioral interventions, Seeking Safety and Relapse Prevention, were both effective at reducing PTSD and AUD symptoms for women with comorbid PTSD/AUD. In general, despite strong evidence of efficacy for treatment of PTSD, there has been little published examining the utility of frontline PTSD treatments for those with comorbid AUD. Early work on a frontline intervention for PTSD, prolonged exposure (PE), involving another SUD group (cocaine dependence) with comorbid PTSD, found unacceptably high rates of dropout (Brady, Dansky, Back, Foa, & Carroll, 2001). However, Foa and colleagues are currently evaluating PE with and without naltrexone for individuals with comorbid alcohol dependence and PTSD, and the results of this trial may indicate that PE is a viable intervention for this group (see Foa & Williams, 2010). Additionally, promising work is being done by Chard and colleagues using cognitive processing therapy (CPT) for PTSD among veterans with comorbid PTSD and SUD in residential treatment settings (Kaysen, Schumm, Pederson, Siem, & Chard, 2011b). An initial open-trial pilot test of CPT by this group found that of the 536 veterans treated with CPT, 49% had current or past AUD. Those with AUD did not differ from the PTSD-only group in treatment participation or in PTSD improvements over the course of treatment. However, this study did not include measures of alcohol use or alcohol problems; therefore, the impact of CPT on alcohol outcomes remains an open question. Thus, interventions have been developed both for AUD and for PTSD that emphasize coping skills and may help break the link between exacerbations in specific PTSD symptoms and increased alcohol cravings. Future studies could consider building on this methodology and using IVR to test whether skills taught in treatment then serve to weaken the associations we have found.
In conclusion, this preliminary study demonstrates that it is possible to evaluate the relationships between PTSD symptoms and alcohol craving in a socially unstable (i.e., over 44% were homeless during their participation in the study) treatment-seeking sample. The findings suggest that the hyperarousal symptoms of anger/irritability and startle are particularly associated with same-day alcohol craving, and that nightmare disturbance, greater emotional numbing, and greater hypervigilance are associated with greater alcohol craving the next day, while greater anger is associated with lower craving the next day. These results, along with the finding that craving did not predict next-day PTSD symptoms, are generally consistent with the self-medication hypothesis. Future research in this area could profitably explore more subtle formulations of the self-medication hypothesis, such as the cognitive dampening attention allocation model (see Armeli, Todd, & Mohr, 2005; Sayette, 1993), to better understand the mechanisms involved. Additionally, identification of moderators of self-medication, such as drinking to cope or drinking to enhance positive affect, would be useful to evaluate via “micro” longitudinal studies to better identify who is particularly at risk for relapse.
Footnotes 1 All GEE analyses were rerun, removing the three participants who did not screen positive for PTSD at intake, as assessed by the PCL-C. Different findings emerged only for the model of individual PTSD symptoms predicting next-day alcohol craving. After removing those participants not screening positive for PTSD, increased distressing dreams, b = 0.07, IRR = 1.08, p < .05, and lower anger/irritability, b = −0.06, IRR = 0.94, p < .05, significantly predicted greater craving the next day. However, increased hypervigilance, b = 0.06, IRR = 1.06, p = .07, and emotional numbing, b = 0.05, IRR = 1.05, p = .10, only marginally predicted greater next-day craving.
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Submitted: July 22, 2011 Revised: December 12, 2011 Accepted: December 14, 2011
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 724-733)
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Record: 61- Temporal variation in facilitator and client behavior during group motivational interviewing sessions. Houck, Jon M.; Hunter, Sarah B.; Benson, Jennifer G.; Cochrum, Linda L.; Rowell, Lauren N.; D'Amico, Elizabeth J.; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 941-949. Publisher: American Psychological Association; [Journal Article] Abstract: There is considerable evidence for motivational interviewing (MI) in changing problematic behaviors. Research on the causal chain for MI suggests influence of facilitator speech on client speech. This association has been examined using macro (session-level) and micro (utterance-level) measures; however, effects across sessions have largely been unexplored, particularly with groups. We evaluated a sample of 129 adolescent Group MI sessions, using a behavioral coding system and timing information to generate information on facilitator and client speech (CT; change talk) within 5 successive segments (quintiles) of each group session. We hypothesized that facilitator speech (open-ended questions and reflections of CT) would be related to subsequent CT. Repeated measures analysis indicated significant quadratic and cubic trends for facilitator and client speech across quintiles. Across quintiles, cross-lagged panel analysis using a zero-inflated negative binomial model showed minimal evidence of facilitator speech on client CT, but did indicate several effects of client CT on facilitator speech, and of client CT on subsequent client CT. Results suggest that session-level effects of facilitator speech on client speech do not arise from long-duration effects of facilitator speech; instead, we detected effects of facilitator speech on client speech only at the beginning and end of sessions, when open questions, respectively, suppressed and enhanced client expressions of CT. Findings suggest that clinicians must remain vigilant to client CT throughout the group session, reinforcing it when it arises spontaneously and selectively employing open-ended questions to elicit it when it does not, particularly toward the end of the session. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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Temporal Variation in Facilitator and Client Behavior During Group Motivational Interviewing Sessions
By: Jon M. Houck
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico;
Sarah B. Hunter
RAND Corporation, Santa Monica, California
Jennifer G. Benson
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
Linda L. Cochrum
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
Lauren N. Rowell
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
Elizabeth J. D’Amico
RAND Corporation, Santa Monica, California
Acknowledgement: Jon M. Houck is a trainer of motivational interviewing language coding systems who is occasionally compensated for the training.
Research reported in this publication was supported by the National Institute on Drug Abuse and the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under award numbers R01DA019938 and R21AA020546, (Principal Investigator: Elizabeth J. D’Amico), and R03DA035690 and K01AA021431 (Principal Investigator: Jon M. Houck). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Motivational interviewing (MI), a directional, client-centered intervention for problematic health behaviors (Miller & Rollnick, 1991, 2002, 2012) has accumulated considerable evidence of its efficacy, both for adults (Hettema, Steele, & Miller, 2005; Lundahl, Kunz, Brownell, Tollefson, & Burke, 2010) and for adolescents (Cushing, Jensen, Miller, & Leffingwell, 2014; Jensen et al., 2011). Empirical support has been found for a theoretical mechanism for MIs effectiveness (Miller & Rose, 2009), specifically, a causal chain linking within-session facilitator speech, client speech, and substance use outcomes (Moyers, Martin, Houck, Christopher, & Tonigan, 2009). Later studies have also fully or partially replicated this seminal finding (Barnett et al., 2014; Morgenstern et al., 2012; Pirlott, Kisbu-Sakarya, DeFrancesco, Elliot, & MacKinnon, 2012; Vader, Walters, Prabhu, Houck, & Field, 2010). Studies examining specific links of the causal chain have also shown robust effects, such as the link between within-session client and facilitator speech (Barnett et al., 2014; Gaume, Bertholet, Faouzi, Gmel, & Daeppen, 2010; Glynn & Moyers, 2010; Moyers & Martin, 2006) and the link between within-session client speech and outcomes (Apodaca et al., 2014; Barnett et al., 2014; D’Amico et al., 2015; Gaume et al., 2010; Shorey, Martino, Lamb, LaRowe, & Santa Ana, 2015; Vader et al., 2010). Most of this work has been conducted using individual sessions except for a recent study by D’Amico et al. (2015) and a subsequent study by Shorey et al. (2015). Thus, little is known about how facilitator and client speech covary over the course of a Group MI session, and how the group therapeutic intervention may be optimized to support behavior change. This is especially important given that group modalities are commonly used in addiction treatment settings (Price et al., 1991; United States Department of Health and Human Services. Substance Abuse and Mental Health Services Administration. Office of Applied Studies, 2014).
One approach to evaluating the mutual influence of facilitator and client speech is by examining the temporal associations between these behaviors through sequential coding. Specifically, given that a particular behavior has occurred, what is the very next behavior that will occur? The first MI study to apply this approach in an individual session (Moyers & Martin, 2006) found that facilitator speech consistent with MI (i.e., affirmations, support, advice with permission, open questions, and reflections) was significantly more likely than expected by chance to be followed by client change talk (CT), a type of within-session client speech that favors changing a problematic health behavior. In contrast, facilitator speech inconsistent with MI (i.e., confrontation, direction, warning, and advice without permission) was significantly more likely than expected by chance to be followed by client sustain talk (ST), a type of within-session client speech that favors maintaining a problematic health behavior. Subsequent studies examining individual therapy have consistently found that facilitator reflections of CT are likely to be followed by client CT, whereas facilitator reflections of ST are likely to be followed by client ST (Barnett et al., 2014; Gaume et al., 2010; Moyers et al., 2009). These findings have been replicated in the group setting, with the additional finding that open-ended questions (OQ) are likely to be followed by CT (D’Amico et al., 2015). These effects support the immediate (i.e., next utterance) influence of facilitator speech on client speech. However, longer-term effects within a therapeutic session have been relatively unexplored. Understanding how to structure and facilitate talk across a therapeutic session may help to optimize client behavioral change after therapy.
Longer-term associations between facilitator and client speech within sessions can be examined by breaking the session into smaller units, such as fifths (i.e., quintiles) or tenths (i.e., deciles) of a session. A seminal study by Amrhein and colleagues (Amrhein, Miller, Yahne, Palmer, & Fulcher, 2003) used this approach to examine individual motivational enhancement therapy (MET; a variant of MI that incorporates feedback) sessions and found not only that CT strength (i.e., a Likert rating of CT strength) predicted drug use treatment outcomes, but also that this effect was only significant for CT in the 7th and 10th deciles, suggesting that particular portions of the session may represent critical periods of influence on client treatment outcomes. Using similar methodology for individual sessions, Walker, Stephens, Rowland, and Roffman (2011) found that in deciles related to when clients received personalized feedback, client CT was particularly predictive of outcome. However, the particular deciles included were not reported. A study of cocaine use applying the decile approach (Aharonovich, Amrhein, Bisaga, Nunes, & Hasin, 2008) found that overall CT strength predicted use and that the shift in CT strength from the 5th to the 10th deciles predicted treatment retention for individuals. A close replication of Amrhein’s work (Morgenstern et al., 2012) examined the effects of CT at the end of an individual session only (i.e., in deciles 9–10) and did not find a significant association between CT in these deciles and outcomes. Finally, a study of significant other effects in MI examined facilitator, client, and significant other speech across deciles and found that only CT from the significant other, and not facilitator speech, predicted client CT (Apodaca, Magill, Longabaugh, Jackson, & Monti, 2013).
Clearly, studies applying the decile technique have taken diverse approaches and shown inconsistent results. In addition, each of these studies has examined this question in individual, rather than group, sessions. The importance of CT from segments of Group MI sessions, and the association between facilitator and client speech during these segments, remains an open question. Rather than examine effects on outcome, an important first step may be the assessment of how facilitator and client speech relate over time in Group MI sessions to provide a theory-driven rationale for segment selection and an explanation for potential effects on outcomes. For instance, if the association between facilitator speech and client CT is consistent throughout the session, then facilitators can maintain a high level of CT by eliciting and reflecting CT from group members throughout the session. If this association varies depending upon the segment of the session, then particular moments during the session may be more important, requiring specialized strategies to ensure that group member CT is elicited and reflected during these critical times.
The present study addresses this question by examining changes in the mutual influence of within-session facilitator and client speech over the course of Group MI sessions with adolescents. Although sequential analysis would appear an attractive choice, the relative infrequency of client change language (Moyers et al., 2009, see Supplemental Materials) limits the number of transitions involving client change language during a segment. Because of the requirement that at least five instances of the transitions of interest occur (Wickens, 1982), it is not feasible to examine transition probabilities for facilitator speech and client change language by segments. Instead, we examined longer-term effects of facilitator speech by segmenting sessions into five equal parts (i.e., quintiles). Because previous studies of individual MI sessions have found quadratic slopes for CT within sessions (Amrhein et al., 2003), we hypothesized that group sessions would show a similar pattern. In addition, we hypothesized that, because of the natural development of CT as well as variability in facilitator reinforcement of CT, the associations between CT and reflections of CT (i.e., RefCT) and between CT and open questions (i.e., OQ) would vary across quintiles.
Method Study Setting
This study involves secondary analysis of data collected in a randomized clinical trial of a group intervention for adolescents in a Teen Court setting (D’Amico, Hunter, Miles, Ewing, & Osilla, 2013). Youth who committed a first-time alcohol or other drug (AOD) offense and were deemed by the Probation department as not in need of more intensive intervention were offered the chance to participate in the community-based Teen Court diversion program. Given that this was a first-time offense, these youth were not further processed by the Probation department. The Teen Court program is not part of the juvenile justice system (i.e., Teen Court is not a drug court), and youth in the Teen Court program are not considered a prison population as they are not formally on probation. Youth could choose to end their participation in the study at any time without any negative consequences; study participation was not tied to youth status in the juvenile justice system. Youth who elect to participate in the Teen Court program enter into a contract with the Teen Court in which they agree to abide by the decisions of a peer jury. Youth who do not wish to participate in Teen court retain the right to have a closed hearing in Juvenile Court. For those who decide to go the teen court, the peer jury is provided with sentencing guidelines, including sanctions such as community service, service on the Teen Court jury, and fees. However, if a teen does not fulfill their contract, the community-based Teen Court lacks the authority to impose any legal consequences; instead, the consequence that has already been imposed by the justice system remains in place (i.e., the offense remains on the youth’s record.). Youth who chose to participate in the study received six group intervention sessions. The current manuscript is based upon examination of behavior during the group intervention sessions, Free Talk. Youth who chose to end their participation in Free Talk could complete any remaining sessions in usual care. As is typical of early intervention programs (see, e.g., McCambridge, Slym, & Strang, 2008), completion rates for both Free Talk and usual care were high; in both cases around 95%. Despite similar completion rates, the rate of recidivism in the following year was much higher for usual care than for Free Talk participants (D’Amico et al., 2013). Usual care participants and sessions were not audio recorded and were, therefore, not included in the analyses. We coded 135 Free Talk sessions. Six sessions were used in coder training; 129 sessions were used for the analyses.
Participants
Youth were eligible if they were 14–18 years old, chose to participate in the Teen Court program during the study period (January 2009 to October 2011) for a first-time AOD offense, and agreed to be randomized to one of the study conditions and complete the study survey instruments. We excluded youth who did not speak and read English well enough to complete the informed consent and self-administered surveys, as well as youth who had multiple offenses or possession of a medical marijuana card. Study refusals (10%) were mostly because of lack of time or transportation to complete a baseline survey before their first group session (see D’Amico et al., 2013 for the CONSORT diagram). No statistically significant demographic differences were observed between study participants and those that refused to participate. There were 110 youth who participated in the Free Talk group sessions. The average age was 16.75 years (SD = 1.02, range 14–18); 65.5% were male, 51.8% were White, 39.1% were Hispanic, and 9.1% were mixed/other race.
Free Talk group sessions were led by one of five facilitators (all female and White) who were psychology doctoral students with prior at-risk teen work experience. The five facilitators received 40 hrs of MI and Free Talk training delivered by two licensed clinical psychologists affiliated with the Motivational Interviewing Network of Trainers (MINT). All Free Talk groups were digitally audio recorded. These two experts reviewed recordings and provided 1-hr weekly supervision to facilitators. The Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005) was used to monitor intervention fidelity and to provide feedback during supervision.
Intervention
Facilitators delivered a six-session manualized group intervention, Free Talk (D’Amico, Osilla, & Hunter, 2010). Detailed study procedures are available elsewhere (D’Amico et al., 2013). The sessions were offered weekly and enrollment in the intervention was based on a rolling admission, so that attending the second session was not contingent upon attending the first session. Facilitators used MI strategies throughout the sessions. For example, facilitators used willingness and confidence rulers, a motivation-building exercise, to facilitate discussions about participants’ willingness and confidence to change. Session content included an interactive discussion of AOD myths (e.g., using alcohol will make me more sociable), the pathway from abstinence to addiction, effective interpersonal communication strategies, the effects of AOD on the brain, and the contribution of AOD on other risk-taking behavior such as unsafe sex and driving under the influence (see D’Amico et al., 2013). Each group session lasted 55 min and the average group size was five adolescents (M = 4.54, SD = 1.96). Because of the rolling admission approximately one adolescent was new to each group session (M = 1.29, SD = 2.28). Ninety-five percent of youth completed all six sessions within the required 90-day time frame.
Procedures
All procedures were approved by the institution’s Human Subjects Protection Committee (HSPC). Audio recordings of intervention sessions were used for coding. Independent coders previously rated sessions using an objective sequential behavior coding system (MISC 2.5; Houck, Moyers, Miller, Glynn, & Hallgren, 2010) and a computerized coding application (CACTI; Glynn, Hallgren, Houck, & Moyers, 2012). Although this approach is novel in the group setting, the sole major difference in the coding approach in the present study compared with prior sequential coding studies was in the handling of client speech. Because it was not possible to determine from the audio recording which teen was speaking, “client speech” could occur from any teen in the group. For example, if a facilitator asked a question of one teen, another teen might respond with CT, the facilitator might reflect this CT, and yet another teen could respond with additional CT. As in prior sequential coding studies (Barnett et al., 2014; Moyers & Martin, 2006; Moyers et al., 2009), each session was sequentially coded in its entirely, from beginning to end.
Interrater reliability was generally good to excellent, with intraclass correlations (ICC; Shrout & Fleiss, 1979) for CT (ICC = .897), ST (ICC = .954), OQs (ICC = .668), and RefCT (ICC = .728) all in the good to excellent range (Cicchetti & Sparrow, 1981). In addition, the utterance-to-utterance reliability of our coders was k = .67, indicating that our coders agreed on the exact sequence of behaviors approximately 73% of the time (Bakeman, Quera, McArthur, & Robinson, 1997). Subsequent research has suggested that this utterance-to-utterance approach is superior to reliability estimates that are based upon counts (Lord et al., 2015). On the whole, these results indicate very high interrater reliability.
Detailed coding procedures and interrater reliability estimates are available elsewhere (D’Amico et al., 2015). The extraction of data for quintiles was possible because sequential coding using CACTI preserves both the temporal sequence of behaviors and the exact time at which behaviors occurred. Quintiles were constructed by calculating the length of the session (i.e., end time of the final utterance minus the start time of the initial utterance) and dividing by five. Coding data from these quintiles were extracted using time codes embedded in CACTI output files. Each session’s CACTI output file was used to create five separate files containing codes corresponding to these quintiles. Summary measures defined in the MISC manual (Houck et al., 2010) were computed for each quintile including the total CT, ST, and RefCT (i.e., simple reflections of CT + complex reflections of CT), and open-ended questions (OQ). The slope of client change language over the five quintiles was tested using repeated measures multivariate analysis of variance (MANOVA) in SPSS version 22. The association between client and facilitator speech across quintiles was evaluated using a cross-lagged panel analysis (path analysis) in Mplus (Muthén & Muthén, 1998–2015), incorporating group size as a covariate.
ResultsAs hypothesized, repeated measures ANOVA indicated significant quadratic trends for CT (F(1, 125) = 34.91, p < .001) and reflections of CT (F(1, 125) = 34.42, p < .001), and cubic trends for CT (F(1, 125) = 26.81, p < .001), reflections of CT (F(1, 125) = 29.45, p < .001) and open-ended questions (F(1, 125) = 25.47, p < .001). No significant linear trends were detected. A plot of mean CT, reflections of CT, and open-ended questions over quintiles is displayed in Figure 1.
Figure 1. Slope of change talk (CT), reflections of change talk (RefCT), and open questions (OQ) over time.
Path analysis was used to evaluate cross-lagged partial regression paths. This approach can distinguish between the effects of client CT on facilitator speech, and the effects of facilitator speech on client CT. In MI, client CT is a relatively rare type of speech (see Supplemental Material in Moyers et al., 2009). Therefore, because of significant zero inflation on the variables of interest, a zero-inflated negative binomial model was used in Mplus 7.2 (Muthén & Muthén, 1998) to evaluate associations between CT and facilitator speech, while also addressing the nonnormal distribution of these measures (for a tutorial, see Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013). This type of multivariate analysis simultaneously assesses a continuous model (e.g., the association between the number of reflections of CT in one quintile and the number of CT utterances in the next quintile) and a logistic model (e.g., the association between the number of reflections of CT in one quintile and having any CT utterances in the next quintile). Because a zero-inflated model was used, we were also able to examine the association between client CT and having zero utterances of reflections of CT or open-ended questions, and between reflections of CT or open-ended questions and having zero utterances of CT. The analysis was conducted for the five successive quintiles of the session to evaluate causal effects over time (Finkel, 1995; Kenny, 2004; Kenny & Harackiewicz, 1979), separately for reflections of CT and open-ended questions.
Figures 2 and 3 present the model and results for CT and reflections of CT. As hypothesized, significant associations were observed between CT and reflections of CT. However, rather than reflections of CT predicting subsequent CT, the count of CT in several quintiles predicted reflections of CT in subsequent quintiles. The significant paths were between CT and subsequent reflections of CT in quintiles two, three, and four (b = 0.054, t = 2.39, p < .01; b = 0.066, t = 2.711, p < .05; b = 0.078, t = 3.461, p < .05, respectively), and between CT in quintile 4 and CT in quintile 5 (b = 0.062, t = 3.109, p < .05). CT in the second quintile was significantly associated with reflections of CT in the third quintile (b = 0.054, t = 2.390, p < .05). CT in the third quintile was significantly associated with reflections of CT in the fourth quintile (b = 0.066, t = 2.711, p < .05). CT in the fourth quintile was significantly associated with fifth-quintile CT (b = 0.062, t = 3.109, p < .05) and reflections of CT (b = 0.078, t = 3.461, p < .05). Group size was not a significant predictor for client or facilitator speech in any quintile. No other partial regression paths were significant in this model.
Figure 2. Initial cross-lagged panel model of change talk (CT) and reflections of CT (RefCT). Variable names ending in “i” represent the logistic part of the model; all other variables are count variables.
Figure 3. Cross-lagged panel model of change talk (CT) and reflections of CT (RefCT) showing only the significant paths. Variables ending in “i” represent the logistic part of the model; all other variables are count variables. Bayesian Information Criteria (BIC) = 5838.686, log-likelihood = −2779.790. Absolute fit statistics such as root mean square error of approximation (RMSEA) and comparative fit index (CFI) are not available for models incorporating count outcomes. * p < .05, ** p < .025, *** p < .01.
Figures 4 and 5 present the model and results for CT and open-ended questions. As hypothesized, significant associations were observed between CT and open-ended questions. Significant paths were detected between CT and subsequent open-ended questions, between open-ended questions and subsequent CT, between open-ended questions and subsequent open-ended questions, and between CT and subsequent CT. Open-ended questions in the first quintile were negatively associated with CT in the second quintile (b = −0.050, t = −2.35, p < .05), and open-ended questions in the fourth quintile were significantly positively associated with CT in the fifth quintile (b = 0.034, t = 2.313, p < .05). CT in the first quintile was significantly negatively associated with open-ended questions in the second quintile (b = −0.13, t = −2.163, p < .05), and with having zero utterances of CT in the second quintile (b = 0.072, t = 2.067, p < .05). This association with having zero utterances of CT in the subsequent quintile means that sessions with high CT counts in the first quintile were more likely to have no instances of open-ended questions in the second quintile. In addition, CT in the third quintile was positively associated with open-ended questions in the fourth quintile (b = .022, t = 2.404, p < .05), while CT in the fourth quintile was positively associated with CT in the fifth quintile (b = 0.047, t = 2.343, p < .05). Open-ended questions were associated with subsequent open-ended questions across all five quintiles (b = 0.023, t = 2.966, p <. 05; b = 0.025, t = 2.534, p < .05; b = 0.027, t = 3.468, p < .05; b = 0.038, t = 4.228, p < .05, respectively, for first to second, second to third, third to fourth, and fourth to fifth quintile open-ended questions). Finally, group size was positively related to having zero instances of open-ended questions in the first quintile (b = 0.201, t = 2.196, p < .05); that is, larger groups tended to have no open-ended questions in the first quintile. Group size was also positively associated with the number of open-ended questions in the fifth quintile (b = 0.034, t = 2.313, p < .05); that is, larger groups tended to have more utterances of open-ended questions in the fifth quintile. No other partial regression paths were significant.
Figure 4. Initial cross-lagged panel model of change talk (CT) and open questions (OQ). Variable names ending in “i” represent the logistic part of the model; all other variables are count variables.
Figure 5. Cross-lagged panel model of change talk (CT) and open questions (OQ) showing only the significant paths. Variables ending in “i” represent the logistic part of the model; all other variables are count variables. Bayesian Information Criteria (BIC) = 7222.875, log-likelihood = −3416.544. Absolute fit statistics such as root mean square error of approximation (RMSEA) and comparative fit index (CFI) are not available for models incorporating count outcomes. * p < .05, ** p < .025, *** p < .01.
DiscussionThe present study used an advanced behavioral coding approach to assess associations between facilitator and client speech across five segments (quintiles) in a large adolescent sample of Group MI sessions. To our knowledge this is the first published study to apply this technique in adolescent group psychotherapy. We detected significant quadratic slopes for client CT and facilitator open-ended questions and reflections of CT, and cubic slopes for CT and reflections of CT, with decreases from the second to fourth quintiles and an increase from the fourth to fifth quintile, consistent with prior studies (Amrhein et al., 2003). These slopes likely reflect the structure of the sessions, in which evocation of teens’ thoughts about the future and take-home messages from each group occurred at the end of the sessions, leading to increased expression of CT and reinforcement of session material through open-ended questions. Alternatively, high levels of CT at the end of the sessions may simply reflect increased teen engagement in the groups as the sessions drew to a close.
We found evidence of effects of client CT on subsequent client CT and facilitator speech, but saw evidence of cross-quintile effects of facilitator speech on client speech only for open-ended questions, and only at the beginning and end of the sessions. This suggests that the beginning and ending of Group MI sessions may be important in exploring group member ambivalence, and particularly for eliciting client CT. The early portions of the session appear to set the stage for the group, whereas the final portions of the session seem to indicate the direction of the client’s ambivalence, and may relate to subsequent outcomes (Amrhein et al., 2003).
Given previous findings on within-session speech using conditional probabilities (D’Amico et al., 2015; Gaume et al., 2010; Moyers & Martin, 2006; Moyers et al., 2009) one might expect to see an effect of reflections of CT on client CT over time. However, four previous studies suggest an immediate impact (i.e., at lag zero, the very next utterance) of facilitator speech on client speech, and cannot address longer-term effects. In contrast, the present study used a cross-lagged approach to examine associations over time at the quintile level. The absence of any cross-quintile effects of facilitator reflections of CT on client CT, coupled with high correlations between these categories of speech within quintiles, suggests that facilitator influence on client speech via reflections of CT is stronger in the short term. This is consistent with research demonstrating associations between session-level counts of MI-consistent speech and CT (Moyers et al., 2007, 2009). Although skilled facilitators can use reflections of CT to “lend” CT to clients who did not express it spontaneously (Miller & Rollnick, 2012), reflections of CT are more commonly used to reinforce than to elicit CT. The momentum generated by this reinforcement of CT appears not to persist over quintiles, suggesting that facilitators must remain vigilant in their reinforcement of CT throughout the session.
In contrast to findings for reflections of CT, we detected effects of open-ended questions on CT at the beginning and the end of these group sessions. Again this is not surprising given that prior research on within-session speech using conditional probabilities has indicated strong associations between open-ended questions and CT (D’Amico et al., 2015; Moyers et al., 2009). In addition to predicting CT at the beginning (second quintile) and end (fifth quintile) of the session, open-ended questions consistently predicted subsequent OQ across all five quintiles, suggesting that facilitator use of open-ended questions was somewhat more stable than was facilitator use of reflections of CT. However, the direction of the effects of open-ended questions on CT differed across segments: at the beginning of the sessions, open-ended questions suppressed CT, whereas at the end of the sessions open-ended questions enhanced CT. The effects of CT were also negative at the beginning of the session, when first-quintile CT suppressed second-quintile open-ended questions. Some of these effects may be because of session structure. For example, at the beginning of each group, open-ended questions focused on topics such as how teens felt about being in the group and generating rules for the group, whereas at the end of the sessions, open-ended questions were typically about what teens would take away from the group or what stood out to the teens about the group. Thus, evocation about thoughts regarding session materials and activities was more likely to generate CT.
Successful implementation of the coding approach used in the present study requires considerable time, effort, and expertise, and as such has not been previously applied in the group setting. What do we learn about group psychotherapy, then, using this novel approach? First, the structure of the group sessions is apparent both from the slopes of facilitator and teen behavior and from the cross-lagged analyses. Across these 129 groups, CT and reflections of CT increased early in the sessions, dropped in the middle and peaked toward the end of the session; open-ended questions peaked early in the sessions and dropped and remained low through the middle and end of the sessions. Overall, the CT-to-CT effect at the end of the session may reflect the influence of peers in Group MI. Such effects may also be reflected by the high CT-to CT-transition probability in a sequential coding study in Group MI (D’Amico et al., 2015) and in a subsequent Group MI study that specifically examined sequential CT statements from one group member to another, which the authors termed “relatedness” (Shorey et al., 2015). The direct influence of peers in Group MI sessions may be as important as the influence of the facilitator, suggesting that teen-to-teen CT may be an important mechanism of change in Group MI.
In addition, little is known about the influence of other factors in Group MI settings, such as the size of the groups. We found that while group size did not influence youth CT or facilitator reflections of CT, group size did appear to be associated with open-ended questions, such that fewer were asked at beginning of the session and more at the end of the session. We speculate that in larger groups, facilitators may have initially asked fewer open-ended questions at the beginning, when facilitators may have been concerned with managing discussion within the allotted session time, and more open-ended questions at the end, when they were confident that all of the session content had been addressed.
Limitations
Client change language was relatively infrequent in the present sample, as in all prior studies of within-session client speech (e.g., Moyers et al., 2009; see Supplemental Materials). On average, groups offered 136.1 utterances per session (SD = 51.6), of which 28.3 (approximately 22.5%) were classified as CT. When further subdivided into quintiles, the modal frequency of CT is zero, which complicated analyses and interpretation of facilitator-client exchanges. In addition, the group audio recordings did not allow for individuals to be identified; it is unknown whether client speech would follow the same patterns at the individual level.
ConclusionClinician influence on client CT is clearly important at the utterance level, with greater open-ended questions and reflections of CT eliciting more CT and subsequently changing behavior (D’Amico et al., 2015; Moyers & Martin, 2006; Moyers et al., 2009); however, we found no evidence of consistent long-duration effects of facilitator reflections of CT on client speech across segments of Group MI sessions. Instead, we detected effects of facilitator speech on client speech only at the beginning and end of sessions, when OQs, respectively, suppressed and enhanced client expressions of CT. Therefore, results emphasize that in Group MI sessions clinicians cannot coast on the strength of an initial, rewarding exchange, with many client expressions of CT and facilitator reflections of CT, but rather must remain vigilant throughout the session to reinforce client change language, using open-ended questions to elicit CT if the client ceases to offer it spontaneously, particularly near the end of the session.
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Submitted: February 11, 2015 Revised: June 3, 2015 Accepted: June 4, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 941-949)
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Record: 62- The association between nonmedical use of prescription drugs and extreme weight control behavior among adolescents. Owens, Sherry L.; Zullig, Keith J.; Divin, Amanda L.; Johnson, Emily; Weiler, Robert M.; Haddox, J. David; Psychology of Addictive Behaviors, Vol 31(5), Aug, 2017 pp. 560-569. Publisher: American Psychological Association; [Journal Article] Abstract: Although extreme weight control behavior (EWCB) is associated with substance use, no research has examined the association between the nonmedical use of prescription drugs (NMUPD) and EWCB. Self-report data were collected from a sample of 4,148 students in Grades 9–12 enrolled in 5 high schools across the United States. Logistic regression models were constructed to examine the nonmedical use of prescription pain relievers, depressants, stimulants, and a composite measure for any NMUPD, and the EWCB of fasting, use of diet pills, powders, or liquids, and vomiting or laxative use. Models were estimated before and after controlling for key covariates for males and females. Approximately 16% of respondents reported any EWCB during the past 30 days, while 11% reported any NMUPD during the past 30 days. After covariate adjustment, any NMUPD was associated with any EWCB in both males and females (p < .05), and all EWCB remained significant in females who reported prescription pain reliever use (p < .01), with 2 out of 3 remaining significant for prescription stimulant and depressant use (p < .01). The only significant association detected for males was between prescription pain reliever use and using diet pills, powders, or liquids (OR = 2.2, p < .01). Results suggest significant associations between NMUPD and EWCB, with variations by sex. These findings provide directions for additional research and point to several potential identification and intervention efforts. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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The Association Between Nonmedical Use of Prescription Drugs and Extreme Weight Control Behavior Among Adolescents
By: Sherry L. Owens
Department of Social and Behavioral Sciences, West Virginia University School of Public Health
Keith J. Zullig
Department of Social and Behavioral Sciences, West Virginia University School of Public Health;
Amanda L. Divin
Department of Health Sciences & Social Work, Western Illinois University
Emily Johnson
Vandal Health Education, University of Idaho
Robert M. Weiler
Department of Global and Community Health, George Mason University
J. David Haddox
Purdue Pharma LP, Stamford, Connecticut, and Department of Public Health and Community Medicine, Tufts University School of Medicine
Acknowledgement: We thank Gerry Hobbs of West Virginia University for assistance with data interpretation. Supported in part by Grant NED 1022329 from Purdue Pharma LP, Stamford, Connecticut, to Robert M. Weiler. This research was originally presented at the 88th National Meeting of the American School Health Association, Portland, Oregon, in October 2014.
The nonmedical use of prescription drugs (NMUPD) ranks second only to marijuana as the most commonly used illicit drug in the United States (US) according to the Substance Abuse and Mental Health Association (SAMHSA, 2013). Their perceived safety, purity, predictable dose response, and psychopharmacological specificity make prescription drugs particularly attractive for experimentation and nonmedical use (Cicero, Inciardi, & Munoz, 2005; Friedman, 2006), as does their ease of accessibility (Wright et al., 2014). NMUPD, at the time of this study, was defined as taking prescription pain relievers, stimulants, and/or depressants without a prescription or solely for the feeling or experience caused by the drug (Center for Behavioral Health Statistics & Quality, 2015; Ford & Watkins, 2012). SAMHSA has subsequently replaced nonmedical use with “misuse” (Hughes et al., 2016). In 2013, approximately 57% of drug overdose deaths were attributed to pharmaceuticals. Of these deaths, 75% were attributed to opioids and 53% were attributed to depressants alone or in combination (Jones, Mack, & Paulozzi, 2013). Opioid overdoses result in 91 fatalities per day in the US (Okie, 2010), while some evidence indicates that highly addictive stimulant use is increasing as well among adolescents (Dalsgaard, Mortensen, Frydenberg, & Thomsen, 2014; Center for Behavioral Health Statistics & Quality, 2015).
Prescriptions for opioid-based pain relievers, stimulants, and depressants have increased dramatically in the past 20 years, with opioid prescriptions increasing nearly fourfold nationally (Paulozzi, Budnitz, & Xi, 2006; Paulozzi, Jones, Mack, & Rudd, 2011). Psychiatrists’ visits by youth in the US nearly doubled between 1995 and 1998 and 2007–2010, outpacing the increase in adult visits during the same time periods (Olfson, Blanco, Wang, Laje, & Correll, 2014). As such, psychotropic medicine prescriptions increased overall in this population, including a variety of stimulants and depressants. These trends have specific consequences for adolescents, as nonmedical use of opioids has increased in corresponding increased trends in prescribing (Okie, 2010; Paulozzi et al., 2006) and prescription drug availability within adolescent networks increases opportunities for nonmedical use. In fact, approximately 18% of adolescents prescribed opioid pain relievers in 2011–2012 reported misusing them (McCabe, West, & Boyd, 2013).
This trend in NMUPD among adolescents warrants an investigation of its potential behavioral correlates so that risk factors and points of intervention can be identified. In the current study, we investigate whether NMUPD is a potential risk factor for extreme weight control behavior (EWCB) because of its known associations with a range of other substance use-related behaviors in adolescents, including alcohol, marijuana, cocaine, and cigarettes (Piran & Robinson, 2006a; Kelly-Weeder, 2011; Lange & Fields, 2015; Pisetsky, Chao, Dierker, May, & Striegel-Moore, 2008). Accordingly, these associations suggest that EWCB may also be a comorbidity of the nonmedical use of prescription drugs (Field et al., 2012).
EWCB is a class of disordered eating behaviors that include using diet pills and/or laxatives, vomiting or laxative use after eating, and skipping meals or fasting to lose or control weight (Story, Neumark-Sztainer, Sherwood, Stang, & Murray, 1998). Because both eating- and substance use-related behaviors involve activation of overlapping areas of the brain, the feelings of reward and reinforcement experienced by the user are also quite similar (Grall-Bronnec & Sauvaget, 2014). Moreover, engagement in both behaviors may also share the similar motivation of self-medication of psychological distress (Abbate-Daga, Amianto, Rogna, & Fassino, 2007).
EWCB is a warning signal of potentially life-threatening eating disorders and as such, have historically been studied as early indicators of full syndrome eating disorders such as anorexia nervosa or bulimia nervosa (Steinhausen, Gavez, & Metzke, 2005). Eating disorders have the highest mortality rates of all psychological disorders (Piran & Robinson, 2006a). Of significance, studies have also found that NMUPD and EWCB share many characteristics such as depression/negative affect (Fernandez-Aranda et al., 2007), anxiety (Godart et al., 2006), self- destructive behavior, denial, (American Psychiatric Association, 2013), obsessive–compulsive disorder and behavior, intense cravings, social isolation, increased risk for suicide and other substance use (Boyes, Fletcher, & Latner, 2007; Kelly-Weeder, 2011; Young, Glover, & Havens, 2012). Therefore, understanding whether NMUPD and EWCB co-occur is critical in developing interventions to ameliorate both of these potentially harmful behaviors in adolescents, a population that is particularly vulnerable to both drug use and disordered eating. What remains unknown from the extant literature is whether NMUPD increases the odds of EWCB in adolescents. Given the high mortality for both drug overdoses and eating disorders in adolescents, determining whether an overlap exists could present opportunities for early intervention.
Gender differences are also important to consider when evaluating the potential association between NMUPD and EWCB. For example, females engage in EWCB at roughly twice the rate of males, which is attributed in some degree to differences in cultural body standards, media exposure, and even genetic predisposition (Martin, 2010). Female adolescents are also more likely to diet, report body dissatisfaction, and engage in EWCB at alarming rates, although body dissatisfaction among male adolescents is growing as well (Neumark-Sztainer, Wall, Larson, Eisenberg, & Loth, 2011). In contrast, adolescent males have historically engaged in more frequent substance use than adolescent females (Havens, Young, & Havens, 2011; Schroeder & Ford, 2009).
The association between EWCB, substance use, and gender in adolescents has also been observed in previous research. For instance, cigarette smoking and alcohol use are consistently correlated with EWCB among both genders (Croll, Neumark-Sztainer, Story, & Ireland, 2002; Pisetsky et al., 2008). However, a review of 25 studies by Young et al. (2012) concluded that females were more likely to report the nonmedical use of prescription pain relievers and tranquilizers than males. Thus, there is reason to believe differences might also exist by gender when examining the association between NMUPD and EWCB. Moreover, as body dissatisfaction patterns change over time among both male and female adolescents, it is critical to update the literature on this topic (Martin, 2010).
To address this gap in the literature, the current investigation examined the association between the NMUPD and EWCB among a large, geographically diverse sample of adolescents. Given the known association between substance use and EWCB, we hypothesized that NMUPD would be significantly associated with EWCB. Moreover, given prior research suggesting a that greater proportion of females engage in EWCB than males, analyses were stratified by gender. Understanding whether NMUPD and EWCB co-occur is critical in developing interventions to ameliorate both of these potentially harmful behaviors in adolescents.
Method Participants
Data were collected during fall 2010 and spring 2011 from a convenience sample 4,148 students in Grades 9–12 attending five public high schools in five states (California, Florida, Illinois, New Jersey, and West Virginia) using a group-administered, anonymous, cross-sectional survey. Data were originally collected as part of a psychometric study examining the reliability of NMUPD items designed for the Youth Risk Behavior Survey questionnaire (Centers for Disease Control and Prevention [CDC], 2013). The schools for this study were intentionally selected to collect data from a geographically, racially, ethnically, and culturally diverse group of participants to support the aims of the investigation (Weiler et al., 2012). Month, days, and class periods of data collection differed to accommodate school schedules. Schools received a stipend and a needs assessment report for their participation. Informed consent was obtained using a protocol approved by the University of Florida’s Institutional Review (#2010-U-0960).
Measures
Independent variables included three items designed to measure the nonmedical use of prescription drugs during the past 30-days (Howard, Weiler, & Haddox, 2009): “During the past 30 days, how many times did you use a prescription pain reliever that was NOT prescribed for you or that you took only for the experience or feeling it caused?”; “During the past 30 days, how many times did you use a prescription depressant that was NOT prescribed for you or that you took only for the experience or feeling it caused?”; and “During the past 30 days, how many times did you use a prescription stimulant that was NOT prescribed for you or that you took only for the experience or feeling it caused?” Participants were also provided with examples of specific clinical and slang terms of each of the drugs before each question was asked. Ordinal response options for each item duplicated the 30-day prevalence response options for the substance use items comprising the Centers for Disease and Control’s (CDC) Youth Risk Behavior Survey (YRBS) and were “0 times”, “1 or 2 times”, “3 to 9 times”, “10 to 19 times”, “20 to 39 times”, and “40 or more times”. Owing to small frequencies in categories above “1 to 2 times”, participants who reported “0 times” were coded as 0 while students who reported “1+ times” were coded as 1 for analysis, with the referent group being those who reported not engaging in NMUPD. These questions demonstrated adequate test–retest reliability in previous research (Howard et al., 2009; Weiler et al., 2012). In the most recent reliability study, the NMUPD items demonstrated fair to substantial reliability as measured by Kappa: .34, .43, and .52 for past 30-day use of simulants, depressants, and pain relievers, respectively (Weiler et al., 2012).
Dependent variables included three items from the YRBS designed to measure EWCB: “During the past 30 days, did you go without eating for 24 hours or more (also called fasting) to lose weight or to keep from gaining weight?”; “During the past 30 days, did you take diet pills, powders, or liquids without a doctor’s advice to lose weight or keep from gaining weight? (Do not include meal replacement products such as Slim fast.)”; During the past 30 days, did you vomit or take laxatives to lose weight or to keep from gaining weight?” Categorical response options for each question were also the same as found on the YRBS and were “Yes” or “No.” Students who reported not engaging in any of the EWCBs (i.e., “No”) were coded as 0, while students who reported engaging in any EWCB (i.e., “Yes”) were coded as 1 for analysis, with the referent group being those who did not report engaging in any EWCB. Previous research has demonstrated adequate test–retest reliability for the EWCB items (Brener et al., 2002).
Data Analysis
Data were analyzed using PC-SAS version 9.3. Analyses included descriptive summaries, followed by inferential analyses to examine the association between NMUPD and EWCB. Multiple logistic regression models were constructed. The first included nonmedical use of any of the prescription drugs measured (pain relievers, stimulants, and depressants), followed by models for each of the individual prescription drug class stratified by sex. All models were estimated before (unadjusted) and after controlling for the selected covariates (adjusted). Covariates included race, age, self-reported grade, past 30-day cigarette smoking, past 30-day alcohol use, past 30-day marijuana use, and self-reported depression and were not recoded for analysis. Demographic covariates were selected based on research demonstrating that EWCB varies by age and race (Croll et al., 2002). Behavioral covariates were chosen because adolescents who engage in EWCB are at elevated risk for cigarette smoking, marijuana use, and alcohol use (Kelly-Weeder, 2011; Lange & Fields, 2015; Piran & Robinson, 2006a; Piran & Robinson, 2006b; Pisetsky et al., 2008); and depression (Crow, Eisenberg, Story, & Neumark-Sztainer, 2008; Messina et al., 2014; Perez, Joiner, & Lewisohn, 2004; Santos, Richards, & Bleckley, 2007).
Results Sample Characteristics
Demographic characteristics are presented in Table 1. The sample included 4,148 students, of which 51.7% (n = 2,144) identified as female and 48.3% (n = 2,004) male. The majority of the sample identified as White (48.1%, n = 1,997), Black (19.0%, n = 788), or Hispanic (14.0%, n = 579). Any NMUPD during the past 30 days was reported by 11.0% (n = 458) of the sample with no differences by sex emerging. To put any NMUPD into perspective, 36.3% (n = 1507) reported alcohol use in the last 30 days, 21.9% (n = 908) reported marijuana use, and 16.0% (n = 664) reported smoking cigarettes.
Sample Characteristics
Chi-square tests were used to test for gender differences among demographic, EWCB, NMUPD, and covariate variables. Females were significantly more likely than males to report any EWCB (20.9% vs. 10.4%, p < .01) and all EWCB variables were significantly more prevalent among females than males (p < .01). Fasting was the most common behavior for both females (16.3%, n = 350) and males (7.5%, n = 151).
Overall NMUPD and pain reliever use did not differ by gender, with 11.1% of males and 11.0% of females reporting overall NMUPD use (p = .386) and 9.3% and 9.5% of male and female respondents, respectively, reporting pain reliever use (p = .975) during the past 30 days. However, depressant use was significantly more prevalent among males (4.8% vs. 3.7%, p = .04), as was stimulant use (3.9% vs. 2.5%, p < .01).
Regression Results
Table 2 presents unadjusted and adjusted odds ratios (ORs) between any NMUPD and the three EWCB by gender. All unadjusted ORs revealed significant associations between any NMUPD and EWCB for both males and females (p < .01). Males who reported any NMUPD during the past 30 days were between approximately 2.6 (fasting) and 4.0 (vomiting or using laxatives) times more likely to report EWCB when compared to males who did not report any NMUPD (p < .01). Females who reported any NMUPD during the past 30 days were between approximately 3.2 (fasting) and 4.8 (diet pills, powders, or liquids) times more likely to report EWCB when compared to females who did not report any NMUPD (p < .01). When adjusted for demographic and behavioral covariates, results were attenuated as expected. For example, males who reported any NMUPD during the past 30 days were 1.7 times more likely to report fasting when compared to males who did not report any NMUPD (p < .05). Additionally, all three EWCB variables remained statistically significant for females who reported any NMUPD during the past 30 days with ORs of approximately 1.9 (fasting), 2.4 (vomiting or using laxatives), and 3.1 (diet pills, powders, or liquids; p < .01).
Unadjusted and Adjusted Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for Reporting Extreme Weight Control Behavior (EWCB) in the Past 30 Days for Any Nonmedical Use of Prescription Drugs (Past 30 Days) by Gender
Table 3 presents unadjusted and adjusted ORs for the nonmedical use of prescription pain relievers, stimulants, and depressants and each EWCB, by gender. Consistent with the results in Table 2 for any NMUPD, results in the adjusted models were tempered, but a distinct pattern emerged for females. For example, males who reported the nonmedical use of prescription pain relievers during the past 30 days were about 2.4 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to males who did not report any nonmedical use of prescription pain relievers (p < .01). In contrast, all but two associations among females (fasting and nonmedical use of prescription stimulants and vomiting or laxative use and nonmedical use of prescription depressants) remained significant in the adjusted models. For example, females who reported nonmedical use of prescription pain relievers during the past 30 days were about 1.8 times more likely to report fasting, 1.9 times more likely to report vomiting or using laxatives, and 2.8 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to females who did not report any nonmedical use of prescription pain relievers (p < .01).
Unadjusted and Adjusted Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for Reporting Extreme Weight Control Behavior (EWCB) in the Past 30 Days, by Nonmedical Use of Pain Relievers, Depressants, and Stimulants, by Gender
These significant associations persisted for nonmedical use of prescription stimulants, where females were about 3.0 times more likely to report vomiting or using laxatives, and 4.5 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to females who did not report any nonmedical use of prescription stimulants (p < .01). Finally, females who reported nonmedical use of prescription depressants during the past 30 days were about 2.2 times more likely to report fasting and 3.2 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to females who did not report nonmedical use of prescription depressants during the past 30 days (p < .01).
To test of whether the association between NMUPD and EWCB among males was stronger for the demographic or behavioral variables, a separate regression was run adjusting for only the demographic variables and compared to the unadjusted model and fully adjusted model containing all covariates for each prescription drug class. Results from this secondary analysis (see online supplemental table) revealed the demographic variables by themselves did not account for the findings for males, suggesting the behavioral variables accounted nearly all of the observed change.
DiscussionThis preliminary study is the first published investigation to examine the association between the nonmedical use of prescription drugs and extreme weight control behavior. EWCB has already been linked to alcohol and marijuana use, and cigarette smoking (Croll et al., 2002; Kelly-Weeder, 2011; Lange & Fields, 2015; Piran & Robinson, 2006a; Piran & Robinson, 2006b; Pisetsky et al., 2008). Study findings also suggest the nonmedical use of prescription pain relievers, stimulants, and depressants appear related to EWCB.
Study findings support both hypotheses that a) NMUPD would be significantly associated with EWCB and b) that associations would vary by gender, even after controlling for key covariates. Results suggest the odds of endorsing any EWCB were higher in adolescents who endorsed any NMUPD. In addition, NMUPD was significantly associated with EWCB for males and females when analyses were stratified by gender and drug class.
Results from the regression analyses must be considered in conjunction with the finding that males and females did not differ in their reported use of any NMUPD or in their nonmedical use of prescription pain relievers. However, males were slightly more likely to report nonmedical use of prescription depressants (4.8%) and stimulants (3.9%) than females (3.7% and 2.5%, respectively). Nonmedical use of prescription stimulants was associated with vomiting/laxative use and or use of diet pills/powders/liquids, but not fasting in females. This is interesting because appetite and weight control have been listed as motives for prescription stimulant use (Suchankova et al., 2013). Our results also contrast with previous studies reporting higher rates of any NMUPD as well as the nonmedical use of prescription pain relievers and sedatives and/or depressants in females (Young et al., 2012). The 2013 YRBS found a higher prevalence of lifetime NMUPD among females in 9th grade and males in 12th grade (CDC, 2014). This study may reflect a growing trend in males’ NMUPD, or may reflect varying rates that one might expect from different sampling techniques. Conversely, the rates of EWCB in this study were only slightly higher than the YRBS estimates (CDC, 2014), as well as those of Ibrahim, El-Kamary, Bailey, and St George (2014) for both genders. In general, past 30-day prevalence for pain reliever use, stimulant use, and depressant use in our study are also higher than past month prevalence rates in the 2011 Monitoring the Future (MTF) survey (Johnston, O’Malley, Bachman, & Schulenberg, 2012). However, there are challenges in making comparisons between our study and MTF, which include but are not limited to, question wording and operationalization of terms, and grade level referencing. Females in this study also endorsed EWCB at twice the rate as males, which is consistent with the 2014 YRBS estimates. Further differences were revealed when analyses were conducted by the individual prescription drug classes.
Nonmedical Use of Prescription Pain Relievers (NMUPPR) and EWCB
Unadjusted models examining NMUPPR and EWCB were significant for both males and females. However, in adjusted models, while all three EWCB variables remained significant for females, only the use of diet pills, powders, and liquids remained significant for males. A possible explanation is that the decrease in significance observed between unadjusted and adjusted models in males resulted from controlling for covariates, as males had markedly higher prevalence rates of all covariates examined with the exception of depression. Therefore, it is plausible that although NMUPPR is independently associated with EWCB in males, once other forms of substance use are accounted for in the model, a more accurate picture for males who report EWCB is that they may be poly users of a variety of substances. This notion is supported by regression findings that adjusted for only the demographic variables (age, grade, and race) for both males and females separately, which remained unchanged from the unadjusted analyses (see supplementary material for table displaying these results).
The findings that fasting and vomiting/laxative use are associated with NMUPPR in females is not surprising given previous research which has examined EWCB and opioid use in those clinically diagnosed with anorexia. For example, Root, Pinheiro, et al. (2010) found significantly higher odds of opioid use in females with anorexia who also engaged in purging behaviors. One possible explanation is that females can be more sensitive to the effects of prescription pain relievers (Whitley & Lindsey, 2009), and that the reinforcing efficacy of prescription pain relievers is enhanced by food deprivation (Root, Pisetsky, et al., 2010) and/or the NMUPPR on an empty stomach.
In both males and females, the use of diet pills, powders, and liquids was significantly associated with the NMUPPR. Moreover, this was the only significant association in males. Diet pills, powders, and liquids often contain large amounts of caffeine or other substances with stimulant properties, which may perpetuate the perception of their use as a fat burning agent. Although speculative, this may be particularly appealing to adolescent males struggling with muscle dysmorphia (Leone, Sedory, & Gray, 2005). The NMUPPR may induce feelings of euphoria (National Institute of Drug Abuse, 2014), a side effect which can be magnified by concurrent use of stimulants such as those found in diet pills, powders, and liquids. Alternatively, the sedating and/or calming effects of prescription pain relievers (Benyamin et al., 2008) may be attractive to individuals who suffer from anxiety or whose use of diet pills, powders, and liquids may cause anxiety. Unfortunately, the causal pathways linking NMUPPR to EWCB cannot be determined in this study, and may vary by type of weight control behavior.
Nonmedical Use of Prescription Stimulants (NMUPS) and EWCB
Moreover, the NMUPS was significantly associated with vomiting/laxative use and use of diet pills/powers/liquids, but not fasting in females, and none of the EWCB in males. This finding is somewhat congruent with the findings of Jeffers, Benotsch, and Koester (2013) and Jeffers and Benotsch (2014) who found NMUPS to be associated with vomiting/laxative use, and the use of diet pills/powders/liquids in samples including both males and females. Our finding is also consistent with Wiederman and Pryor (1996a) who reported that females with bulimia were more likely to use amphetamines than anorexics, and Wiederman and Pryor (1996b) who found greater amphetamine use in females with bulimia. However, our findings differ with Wiederman and Pryor (1996a) who found caloric restriction (e.g., fasting) predicted amphetamine use, and Piran and Robinson (2006a) who found severity of fasting correlated with amphetamine use.
No significant associations were detected between the NMUPS and fasting in either males or females in the adjusted models. This finding is interesting given a known side effect of prescription stimulants is loss of appetite. Loss of appetite or weight loss is a commonly cited motive for the NMUPS, especially among females (Boys, Marsden, & Strang, 2001; Jeffers & Benotsch, 2014; Jeffers et al., 2013; Teter, McCabe, LaGrange, Cranford, & Boyd, 2006). Prescription stimulant use can temporarily disrupt the body’s production of ghrelin, a hunger-generating hormone (Suchankova et al., 2013). We also cannot discount that some study participants did engage in the NMUPS to aid in fasting, although not for a period of 24 or more hours as specified in the questionnaire. Given that individuals who engage in the NMUPS, regardless of motive, are at elevated risk for alcohol and other drug use (Jeffers & Benotsch, 2014; McCabe, Boyd, & Teter, 2009), as well as the current prevalence of the nonmedical use of stimulants and their increased availability (Teter, Falone, Cranford, Boyd, & McCabe, 2010), the strong associations found in the current study do warrant concern. Future research should investigate longitudinal trends in the NMUPS as it relates to EWCB.
Nonmedical Use of Prescription Depressants (NMUPDep) and EWCB
In the present study, NMUPDep was associated with all forms of EWCB in the unadjusted model, but not with any EWCB in the adjusted model for males. For females, both fasting and use of diet pills, powders, or liquids remained significantly associated with NMUPDep in the adjusted model, but not vomiting/laxative use. The findings on fasting are consistent with those of Piran and Robinson (2006a) who found the use of sleeping pills to be associated with fasting in female undergraduates and a community-based sample of young women reporting EWCB (2006b). The lack of a significant association between NMUPDep and vomiting/laxative use is consistent with Piran and Robinson (2006a) who found sleeping pill use higher in women who binged and dieted, but did not purge. There are no direct comparisons between our findings and other studies examining EWCB. However, our findings contrast with a number of studies using clinical samples which have found significantly greater rates of sedative use in individuals diagnosed with anorexia nervosa binge/purge subtype, bulimia nervosa, binge eating disorder, purging disorder, and eating disorders not otherwise specified (Fouladi et al., 2015), greater rates of tranquilizer use in adolescents diagnosed with bulimia nervosa (Wiederman & Pryor, 1996b), and binge eating predicted tranquilizer use in adult females diagnosed with bulimia nervosa (Wiederman & Pryor, 1996a).
Similar to our findings examining the NMUPPR, the current study found a significant association between the NMUPDep and the use of diet pills, powders, and liquids among females. Specifically, females reporting the NMUPDep were 3.2 times more likely to report using diet pills, powders, or liquids when compared to females who did not report any NMUPDep. Because the use of diet pills, powders, and liquids may cause anxiety in some users due to their often high caffeine or other stimulant content, individuals may use prescription depressants for their sedating and/or calming effects. Prescription depressants have a rapid onset of action and generally produce almost immediate effects (Longo & Johnson, 2000), which may explain why they are often the first choice in the treatment of anxiety and/or insomnia (Bostwick, Casher, & Yasugi, 2012). Alternatively, prescription depressants, particularly benzodiazepines, are known to impair aspects of both memory and attention (Buffett-Jerrott & Stewart, 2002). Hence, the use of diet pills, powders, and liquids may be an attempt by some users to reduce sluggishness or ‘hangover’ symptoms which occur as prescription depressants are eliminated from the body (Ashton, 1986; Longo & Johnson, 2000).
Depressants are often prescribed for anxiety disorders, insomnia, anxiety associated with medical illness, anxiety associated with depression, and impulse control disorders (Bostwick et al., 2012; Longo & Johnson, 2000). Motivations most frequently cited for their nonmedical use are to help with sleep, to decrease anxiety, and to get high (Boyd, McCabe, Cranford, & Young, 2006). Given their pharmacological properties, rapid onset of action, motivation(s) for use, perception as safe, and ease of procurement (The Partnership at Drugfree.org, 2013), particularly among females (Paulozzi, Strickler, Kreiner, & Koris, 2015), prescription depressants may be the ‘go-to’ medication for adolescent females who may be unable to obtain alcohol or marijuana, or because prescription depressants are calorie free and do not stimulate appetite.
Unfortunately, adolescent females are particularly vulnerable to prescription medication sharing, particularly with friends who feel they need to self-medicate (Goldsworthy, Schwartz, & Mayhorn, 2008). Both anxiety and insomnia are comorbidities of EWCB, thus NMUPDep, in this case, may be used by adolescent females to address mental health issues associated with EWCB (Wade, Bulik, Neale, & Kendler, 2000). In adolescent females, poor body image is a strong predictor of anxiety and depression, and is also a motivator for EWCB (Boyes et al., 2007). Depressants are often prescribed to treat eating disorders and, therefore, may be used to self-medicate among adolescents (Wade et al., 2000). Given the reinforcing properties of prescription depressants are amplified with nonmedical use (Hoffman & Mathew, 2008) and food deprivation, the combination of both may exert substantial physiological changes on both cognition and mood (Harrop & Marlatt, 2010). Thus, the potential association between the NMUPDep and EWCB detected in this study is a timely phenomenon to examine owing to the high prevalence of both prescription depressant use and adolescents trying to lose weight.
Limitations
Limitations of the present study include not measuring and/or controlling for body mass index (BMI) and/or body image, eating disorder diagnosis, and the self-reported, cross-sectional study design. In addition, no information on extreme exercising was available, which are common among males with body dysmorphia. Second, results may not be generalizable because data were collected from a convenience sample in five states. However, the overall study sample was large and we were able to control for key covariates, which limit the chance of nonmeaningful statistical significance for the study findings (Fischer et al., 2013). Third, analyses were limited to the questions asked and the information collected by self-report. This may have affected the association between NMUPD and EWCB, in addition to introducing potential bias. e.g., although self-report data on risk behaviors and substance have been shown to be generally valid (Ford, 2008), some participants may have provided inaccurate information due to the sensitive nature of the questions. Fourth, caution should be extended when interpreting the results for males in the study owing to a small sample size reporting EWCB behavior. Finally, the temporal sequence between NMUPD and EWCB cannot be determined through our cross-sectional study design. This question may be useful to researchers in predicting NMUPD, EWCB, and/or the factors leading to the growth of either. Despite these limitations, this current study supports the findings of other published studies documenting the prevalence of either the NMUPD or EWCB among adolescents, and is the first to find significant associations among multiple prescription drug classes and EWCB among adolescents.
ConclusionsGiven rates of NMUPD, EWCB, and subsequent adverse outcomes, understanding the correlates of the NMUPD and EWCB is important. Our findings add to the literature by demonstrating the association between any NMUPD, and the nonmedical use of prescription pain relievers, stimulants, and depressants in adolescents and the EWCB of fasting, vomiting/laxative use, and use of diet pills, powders, or liquids, and that these relationships varied by gender.
Although the temporal sequence remains unclear between the NMUPD and EWCB, study findings a) underline the importance of examining individual prescription drug classes used nonmedically when analyzing EWCB; b) demonstrate significant associations between any NMUPD, and the specific nonmedical use of pain relievers, stimulants, and depressants and EWCB; and c) confirm the association between the NMUPD and EWCB is stronger for females than males after adjusting for key covariates.
Implications
Identifying the association between NMUPD and EWCB among adolescents may help schools, practitioners, and professionals working with adolescents identify students at-risk for a myriad of problematic and risky behaviors. Mulheim (2012) found that early intervention is key in preventing severe physical and psychological consequences of EWCB. Specifically, they note skipping lunch as a warning sign that school personnel have the capacity to identify. Therefore, it may be possible to initially detect behavior consistent with early EWCB during school breakfast and lunch, particularly among female students, allowing concerned school personnel to identify adolescents who are at-risk for disordered eating or eating disorders. Furthermore, our findings suggest that these students, particularly adolescent females, may be referred by school personnel for further evaluation of potential NMUPD on a case-by-case basis to determine whether signs of NMUPD use occur in the home or at school alongside EWCB.
In addition to EWCB identification, school or family level interventions targeting NMUPD may benefit by including information about EWCB identification, treatment, and prevention. For example, a meta-analytic review of eating disorder prevention interventions found that programs with a focus on nonspecific factors that may influence eating pathology deserve further exploration (Stice, Shaw, & Marti, 2007). Moreover, a meta-analysis and systematic review of drug use prevention programs found that programs focusing on social skills such as resisting peer pressure significantly reduced drug use at follow-up (Faggiano et al., 2008). While social skills were not explicitly mentioned by Stice et al. (2007), such skills may be an underlying factor worth exploring. The findings of these two studies, in conjunction with our findings on the associations between NMUPD and EWCB may offer an opportunity to implement interventions designed to enhance the types of social skills needed to prevent initiation of either risk behavior.
However, the complexity of the problem associated with NMUPD and EWCB among adolescents presents many challenges. A multifaceted, collaborative, and coordinated response is required. Study findings suggest significant associations between NMUPD and EWCB, with significant variations by gender. Results provide a fundamental starting point for additional research, while also pointing to several potential identification and intervention efforts.
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Submitted: November 28, 2016 Revised: May 15, 2017 Accepted: May 16, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (5), Aug, 2017 pp. 560-569)
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Record: 63- The influence of confidence on associations among personal attitudes, perceived injunctive norms, and alcohol consumption. Neighbors, Clayton; Lindgren, Kristen P.; Knee, C. Raymond; Fossos, Nicole; DiBello, Angelo; Psychology of Addictive Behaviors, Vol 25(4), Dec, 2011 pp. 714-720. Publisher: American Psychological Association; [Journal Article] Abstract: Social norms theories hold that perceptions of the degree of approval for a behavior have a strong influence on one's private attitudes and public behavior. In particular, being more approving of drinking and perceiving peers as more approving of drinking, are strongly associated with one's own drinking. However, previous research has not considered that students may vary considerably in the confidence in their estimates of peer approval and in the confidence in their estimates of their own approval of drinking. The present research was designed to evaluate confidence as a moderator of associations among perceived injunctive norms, own attitudes, and drinking. We expected perceived injunctive norms and own attitudes would be more strongly associated with drinking among students who felt more confident in their estimates of peer approval and own attitudes. We were also interested in whether this might differ by gender. Injunctive norms and self-reported alcohol consumption were measured in a sample of 708 college students. Findings from negative binomial regression analyses supported moderation hypotheses for confidence and perceived injunction norms but not for personal attitudes. Thus, perceived injunctive norms were more strongly associated with own drinking among students who felt more confident in their estimates of friends' approval of drinking. A three-way interaction further revealed that this was primarily true among women. Implications for norms and peer influence theories as well as interventions are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Influence of Confidence on Associations Among Personal Attitudes, Perceived Injunctive Norms, and Alcohol Consumption
By: Clayton Neighbors
Department of Psychology, University of Houston, University of Houston University of Houston;
Kristen P. Lindgren
Department of Psychiatry & Behavioral Sciences, University of Washington
C. Raymond Knee
Department of Psychology, University of Houston, University of Houston University of Houston
Nicole Fossos
Department of Psychology, University of Houston, University of Houston University of Houston
Angelo DiBello
Department of Psychology, University of Houston, University of Houston University of Houston
Acknowledgement: Preparation of this article was supported in part by National Institute on Alcohol Abuse and Alcoholism Grants R01AA014576 and R00AA017669.
Research has emphasized the strong influence of social norms on private attitudes and public behavior. The application of social norms theories to health risk behaviors (e.g., alcohol use) has tended to emphasize descriptive norms (perceptions of the prevalence of a behavior) versus injunctive norms (perceptions of the degree of group approval for a behavior or attitude). Moreover, limited consideration has been given to the extent to which the social norms—health behavior link may depend on the degree of confidence attributed to one's own attitudes and perceptions of others' attitudes. The present research evaluates confidence as a moderator of the associations between one's own attitudes and drinking and between perceptions of others' attitudes (perceived injunctive norms) and drinking among male and female college students.
Social NormsThe operationalization of social norms began with relatively vague descriptions of general customs, traditions, and values (Sherif, 1936). It has evolved to more precise categorizations, with Cialdini and colleagues making an important distinction between descriptive and injunctive norms (Cialdini, Reno, & Kallgren, 1990). Descriptive norms and perceived descriptive norms refer to the actual and perceived quantity of others' behavior, respectively. In contrast, actual and perceived injunctive norms, the primary focus of the present research, refer to the actual and perceived degree of approval that others have about a behavior. Injunctive norms are critical elements in the theories of reasoned action and planned behavior (there referred to as subjective norms; Ajzen & Fishbein, 1980; Armitage & Conner, 2001; Fishbein & Ajzen, 1975, 2010).
According to the theories of reasoned action and planned behavior (Ajzen & Fishbein, 1980; Armitage & Conner, 2001; Fishbein & Ajzen, 1975, 2010), how one intends to behave is a direct function of individuals' own evaluations or attitudes about the behavior (e.g., approval or disapproval) and their perceptions of the degree to which others' approve or disapprove of the behavior. We propose that the degree of confidence that individuals have—whether about their own evaluations of a behavior or whether about their perceptions of the approval of important others—may moderate the influences of attitudes and injunctive norms on behavior.
Social Norms and DrinkingSocial norms have been found to be a strong predictor of alcohol consumption among college students (Borsari & Carey, 2001, 2003; Neighbors, Lee, Lewis, Fossos, & Larimer, 2007; Prentice & Miller, 1993). Although students drink frequently, they tend to overestimate the prevalence and approval of drinking among their peers and the magnitude of discrepancy is associated with heavier drinking (Baer, Stacy, & Larimer, 1991; Borsari & Carey, 2003; Neighbors, Dillard, Lewis, Bergstrom, & Neil, 2006; Perkins & Berkowitz, 1986). No research to date, of which we are aware, has evaluated confidence in one's perceptions of norms as a moderator of the association between perceived norms and drinking.
ConfidenceThe absence of research considering confidence as a moderator of injunctive norms motivated the present study. This was based on the observation that extensive research has documented large inconsistencies in perceptions of drinking norms and actual drinking behavior. Previous research has not examined how accurate students think they are when they provide these estimates and it stands to reason that if they feel they are just guessing, then these perceptions should have relatively little impact on their own drinking. Alternatively, if students believe they are relatively accurate in their perceptions of peer approval, then it would stand to reason that their estimates should have considerably more influence on their behavior.
Although confidence related to norms has not received much empirical attention, confidence related to attitudes has been extensively studied. Attitude confidence refers to one's sense of conviction about (or confidence in) an attitude and is thought to represent one aspect of attitude strength (Tormala & Rucker, 2007). Stronger attitudes have a greater impact on information processing and in guiding behavior, and are more resistant to change over time (Krosnick & Petty, 1995). Attitudes held with high confidence have a substantially stronger average attitude-behavior correlation compared to attitudes held with low confidence (Kraus, 1995). With respect to drinking, we would expect that the more confident one is in his or her attitude about alcohol, the more strongly that attitude will be associated with drinking.
In theory, when one feels more confident in one's estimate of others' approval of a behavior, that perception should more strongly predict how often one engages in that behavior. Several studies have found confidence in one's attitude to be associated with greater attitude—behavior correspondence (Berger & Mitchell, 1989; Fazio & Zanna, 1978; Tormala, Clarkson, & Petty, 2006; Tormala & Petty, 2002) but this effect has not been previously examined in the context of drinking.
Gender and DrinkingGender has also been found to be an important factor in considering alcohol consumption and social norms regarding alcohol use. Research has shown that male college students tend to consume larger quantities, drink more frequently, and more often engage in heavy drinking than female college students (Johnston, O'Malley, Bachman, & Schulenberg, 2008; O'Malley & Johnston, 2002; Read, Wood, Lejuez, Palfai, & Slack, 2004). Gender differences in drinking are also intertwined with gender differences in social norms for drinking. Men and women evaluate the effects of alcohol differently (Neighbors, Walker, & Larimer, 2003), and heavy drinking is more consistent with college student identity for men as compared with women (Lyons & Willot, 2008; Prentice & Miller, 1993). In addition the social consequences of excessive drinking among college students tend to be more positive for men and more negative for women (George, Gournic, & McAfee, 1988; Nolen–Hoeksema, 2004).
What is less clear is whether confidence in perceived approval might have differential effects on the association between perceived injunctive norms and drinking among men and women. On the one hand, we might expect that the confidence–injunctive norm interaction might be more evident among men than women, given that drinking norms tend to be more salient for men (e.g., Prentice & Miller, 1993). On the other hand, previous research suggests that moderators of normative influences on drinking behavior tend to be less evident in the presence of stronger drinking norms. For example, Knee and Neighbors (2002) found that peer influence was moderated by individual differences in self-determination among typical male and female students, but not among fraternity students, where the heavier drinking norm may overshadow individual differences. We were also interested in evaluating whether confidence in one's attitudes might influence the attitude–behavior association differently for women than for men.
The present research was designed to evaluate confidence as a moderator of associations among perceived injunctive norms, own attitudes, and drinking. We expected perceived injunctive norms and own attitudes would be more strongly associated with drinking among students who felt more confident in their estimates of peer approval and own attitudes. We also tested whether these associations varied by gender. Figure 1 presents a conceptual model representing the hypotheses.
Figure 1. Conceptual model. Evaluated interactions are represented by pathways from a variable to a pathway. Thus, the arrow from attitude confidence to the arrow from attitude to drinking represents the prediction that attitude confidence would moderate the association between attitude and drinking. Gender was also evaluated as a moderator of associations between variables and drinking and as a moderator of two-way interactions.
Method Participants
Participants were 708 (60.1% women) undergraduates at a large public university who took part in a longitudinal web-based alcohol intervention study. Participants who met heavy drinking criteria (at least 4/5 drinks on at least one occasion over the previous month for women/men) completed a baseline survey in the Fall of 2005. The present study comes from the 12 month follow-up survey. Participants ranged in age from 18 to 28 (mean [M] = 19.12; standard deviation [SD] = .57). Ethnicity was 65.6% White, 23.5% Asian, and 10.9% classified as other. The majority (86.7%) of the sample were of sophomore class standing at the time of the 12-month follow-up survey.
Procedure
First-year university students were invited to complete a screening survey (N = 4,103). Of those invited, 2,095 (51.1%) participants provided informed consent and completed screening. Students meeting the heavy drinking inclusion criteria (N = 896; 42.7%) were immediately routed to the baseline survey. Of the participants who met study criteria, 818 (91.3%) completed the baseline survey. Participants were contacted via mailed letters, email, and phone calls to complete online surveys at 6 month intervals over a 2-year follow-up period. Data for the current study come from the 12-month follow-up survey (86.6% retention rate). The assessment took approximately 50 minutes to complete and participants were compensated $25. The University's Institutional Review Board approved all aspects of the current study.
Measures
Attitudes
Participants' attitudes toward drinking were measured using items developed by Baer (1994). Participants responded to 4 items assessing their approval of four drinking behaviors: drinking alcohol every weekend, drinking alcohol daily, driving a car after drinking, and drinking enough alcohol to pass out (e.g., “How much do you approve of drinking alcohol daily?”). The response scale ranged from 1 = Strong disapproval to 7 = Strong approval. The items were averaged to create one variable of participants' own approval of risky alcohol use (α = .72).
Perceived injunctive norms
Perceived injunctive norms were measured using the same four items used to measure participants own approval of risky alcohol use, but were revised to ask about participants' perceptions of their friends' approval of their alcohol use (e.g., “How would your friends feel if you drank alcohol daily”). The response scale ranged from 1 = Strong disapproval to 7 = Strong approval. The items were averaged to create one variable of participants' perceived injunctive norms for risky alcohol use (α = .76).
Confidence
Confidence was measured by asking participants to rate their confidence in estimates of one's own approval and friends' approval of drinking. Following the set of items asking about participants' own approval and the set of items asking about perceived injunctive norms, participants were asked to: “Please indicate how confident you are that your responses to the previous items are correct.” Response scales ranged from 1 = Not at all confident to 7 = Absolutely confident.
Alcohol consumption
Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985) was used to measure quantity and frequency of alcohol consumption. Participants were asked to “Consider a typical week during the last three months. How much alcohol, on average (measured in number of drinks), do you drink on each day of a typical week?” Responses consisted of the typical number of drinks participants reported consuming on each day of the week. A weekly drinking variable was calculated by summing responses for each day of the week. The DDQ has demonstrated convergent validity with measures of drinking and good test–retest reliability (Baer et al., 1991; Borsari & Carey, 2000; Neighbors, Larimer, & Lewis, 2004; Neighbors et al., 2006).
ResultsHypotheses were tested with hierarchical negative binomial regression (Cohen, Cohen, West, Aiken, 2003; Hilbe, 2007), an approach that is comparable to hierarchical linear regression with the exception that the outcome follows a negative binomial distribution. Means and standard deviations by gender are presented in Table 1. Zero-order correlations for study measures are presented by gender in Table 2 for descriptive purposes. Drinks per week, a count variable, was specified as the outcome variable. Count variables consist of non-negative integers, which tend to be positively skewed, and are better approximated by a Poisson or negative binomial distribution rather than a normal distribution (Atkins & Gallop, 2007). Analyses were conducted hierarchically with order of entry following priority of theoretical interest. Gender was dummy coded (Men = 1) and centered. All other predictors were mean centered.
Means and Standard Deviations by Gender
Zero-Order Correlations Among Variables
Main effects were entered at step 1 (gender, own approval, confidence in own approval, perceived injunctive norms, and confidence in perceived injunctive norms). Raw and exponentiated parameter estimates with significance tests and confidence intervals are presented in Table 3. Raw parameter estimates are log based. The exponentiated intercept value represents the predicted number of drinks per week for women at average values of one's own approval and confidence in one's own approval (8.31 drinks per week). Exponentiated parameter estimates can be interpreted as rate ratios. Thus, at step 1, the exponentiated parameter estimate for gender is 1.37, indicating that men, on average, consumed 37% more drinks per week than women (11.31 drinks per week). In addition, each unit increase in one's own approval toward drinking was associated with consuming an average of 39% more drinks per week. Confidence in one's own approval was marginally associated with less drinking, by 6% per unit increase. Each unit increase in perceived injunctive norms was associated with 16% more drinks per week. Confidence in perceived injunctive norms was not significantly associated with drinking.
Drinking as a Function of Gender, One's Own Approval, Confidence in One's Own Approval, Perceived Injunctive Norms, and Confidence in Perceived Injunctive Norms
Two-way products evaluating confidence as a moderator were added at step 2 (Own approval × Confidence in own approval and Perceived injunctive norms × Confidence in perceived injunctive norms). Results indicated a significant interaction which was consistent with hypotheses. Specifically, the association between perceived injunctive norms and drinking depended on confidence in perceived injunctive norms, suggesting that the association between perceived injunctive norms and drinking increased by 6.5% for each unit increase in confidence (See Figure 2). Two-way products with gender were added at step 3. There were no significant interactions between one's own approval and confidence in one's own approval, nor were there any significant two-way interactions with gender at step 3.
Figure 2. Drinking as a function of perceived injunctive norms and confidence in perceived injunctive norms for friends. Two-way interaction between confidence in perceived injunctive norms and perceived injunctive norms in predicting drinks per week. Estimates were derived from exponentiated parameter estimates where values for confidence and perceived injunctive norms were systematically substituted in the negative binomial regression equation.
Three-way interactions to evaluate gender as a moderator of interactions between confidence and corresponding approval were added at step 4 (Gender × Own approval × Confidence in own approval, and Gender × Perceived injunctive norms × Confidence in perceived injunctive norms). Results revealed a significant three-way interaction with gender, perceived injunctive norms, and confidence in perceived injunctive norms (see Figure 3). This three-way interaction qualified the two-way interaction between perceived injunctive norms and confidence in perceived injunctive norms identified in step 2. Furthermore, tests of simple two-way interactions confirmed that confidence moderated the association between perceived injunctive norms and drinking among women, t(689) = 2.68, p < .01, but not men, t(689) = −.42, p = .68.
Figure 3. Drinking as a function of gender, perceived injunctive norms, and confidence in perceived injunctive norms for friends. Three-way interaction among gender, confidence in perceived injunctive norms, and perceived injunctive norms in predicting drinks per week. Estimates where derived from exponentiated parameter estimates where values for gender, confidence, and perceived injunctive norms were systematically substituted in the negative binomial regression equation.
DiscussionInjunctive norms were found to be significant, unique predictors of drinking. The present study also extended social norms research and theory by incorporating a construct from the attitude literature—for example, attitude confidence—and investigating it as a potential moderator of the norms—behavior link. Study findings did not support confidence as a moderator of personal approval and drinking behavior but did support confidence as a moderator of the relationship between perceptions of others' approval (injunctive norms) and behavior.
This research provides evidence that estimates of friends' approval of drinking are more strongly associated with own drinking when students feel confident in their estimates. Furthermore, the present findings suggest that confidence in perceived injunctive norms matters more for women. This may reflect an underlying gender difference in the weighing of confidence in perceptions of others' approval. It may, alternatively, vary as a function of the gender specificity of the behavior in question. For example, an opposite pattern of results might be found with thinness norms, which are more salient among women than men (Bergstrom & Neighbors, 2006; Sanderson, Darley, & Messinger, 2002). More generally, the self-relevance of the norm may provide a limiting condition under which confidence in perceptions of others' approval becomes important.
The present research extends previous work indicating that attitude confidence is associated with stronger attitude—behavior relationships (Berger & Mitchell, 1989; Fazio & Zanna, 1978; Tormala et al., 2006; Tormala & Petty, 2002). Findings suggest that confidence may not be universally important in considering attitude—behavior relationships. Indeed, confidence may have a greater impact on attitudes or cognitions that are more ambivalent or in which there is greater subjectivity (e.g., perceptions of others' approval).
The present study also has direct implications for clinical assessment and intervention. Research over the last decade has consistently found support for the importance of social norms, particularly descriptive norms, in predicting college student drinking behaviors (Borsari & Cary, 2003; Lewis & Neighbors, 2004; Neighbors et al., 2010; Neighbors et al., 2004). This study's findings are consistent with previous studies as they indicate the importance and reliability of norms as a predictor of drinking. Future clinical research may benefit from considering injunctive norms as additional targets for assessment and ultimately, intervention. Findings that confidence and gender moderated the norms–drinking relationship also suggest the potential utility of assessing confidence in norms; attempting to reduce confidence (increase uncertainty) in those norms; and developing gender-specific interventions.
It is important to consider this research in light of several limitations. First, it is a single study. Multiple studies will ultimately be needed to evaluate and clarify the extent to which confidence in attitudes and perceived norms for drinking influence subsequent behavior. Second, the measures of attitudes and injunctive norms include a diverse set of target behaviors, which may increase generalizability at the cost of reducing predictive utility for specific behaviors. Additionally, the measure of attitude confidence (e.g., “Please indicate how confident you are that your responses to the previous items are correct”) could have been misinterpreted by some students. Participants may not have been sure whether this referred to confidence in the actual belief versus the accuracy of their response. Future research should clarify the instruction set. Another limitation is that the drinking measure is limited to self-report. The sample used in the present study may also limit generalizeability. All participants were college students and had to meet heavy drinking criteria in order to screen into the study. Thus, the present results may not generalize to abstainers or light drinkers or beyond the college population.
Finally, several future research directions are suggested. The present research focused exclusively on injunctive norms, and it also would be worthwhile to evaluate confidence in the context of descriptive norms. Considering other addictive behaviors, especially those that vary in relevance by gender may also be useful. Finally, experimental manipulations of confidence are needed to provide evidence of causality. In sum, the present research provides both applied and theoretical contributions to the existing literature related to attitudes, norms, and drinking.
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Submitted: September 24, 2010 Revised: August 2, 2011 Accepted: August 11, 2011
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Record: 64- The influence of cultural variables on treatment retention and engagement in a sample of Mexican American adolescent males with substance use disorders. Burrow-Sánchez, Jason J.; Meyers, Kimberly; Corrales, Carolina; Ortiz-Jensen, Cynthia; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 969-977. Publisher: American Psychological Association; [Journal Article] Abstract: Adolescent substance abuse is a serious public health concern, and in response to this problem, a number of effective treatment approaches have been developed. Despite this, retaining and engaging adolescents in treatment are 2 major challenges continuously faced by practitioners and clinical researchers. Low retention and engagement rates are especially salient for ethnic minority adolescents because they are at high risk for underutilization of substance abuse treatment compared to their White peers. Latino adolescents, in particular, are part of the fastest growing ethnic minority group in the United States and experience high rates of substance use disorders. Heretofore, the empirical examination of cultural factors that influence treatment retention and engagement has been lacking in the literature. The goal of this study was to investigate the influence of the cultural variables ethnic identity, familism, and acculturation on the retention and engagement of Latino adolescents participating in substance abuse treatment. This study used data collected from a sample of Latino adolescent males (N = 96), predominantly of Mexican descent, and largely recruited from the juvenile justice system. Analysis was conducted using generalized regression models for count variables. Results indicated that higher levels of exploration, a subfactor of ethnic identity, and familism were predictive of attendance and engagement. In contrast, higher levels of Anglo orientation, a subfactor of acculturation, were predictive of lower treatment attendance and engagement. Clinical implications for the variables of ethnic identity, acculturation, and familism as well as suggestions for future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Influence of Cultural Variables on Treatment Retention and Engagement in a Sample of Mexican American Adolescent Males With Substance Use Disorders
By: Jason J. Burrow-Sánchez
Department of Educational Psychology, University of Utah;
Kimberly Meyers
Department of Educational Psychology, University of Utah
Carolina Corrales
Department of Educational Psychology, University of Utah
Cynthia Ortiz-Jensen
Department of Educational Psychology, University of Utah
Acknowledgement: This research was supported by Award Number K23DA019914 from the National Institute on Drug Abuse awarded to Jason J. Burrow-Sánchez. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health. We thank all members of the Validating Interventions for Diverse Adolescents Research Team at the University of Utah.
Adolescent substance abuse is a serious public health problem with almost 10% of youth, ages 12–17, reporting the use of illicit drugs and 7% of these youth meeting criteria for a substance use disorder as indicated from national survey collected by the Substance Abuse and Mental Health Services Administration (SAMHSA; 2012). In response to this problem, a number of effective treatment approaches have been developed and tested for adolescents over the past two decades (Waldron & Turner, 2008; Williams & Chang, 2000). However, retaining and engaging adolescents in treatment are two major challenges continuously faced by practitioners and clinical researchers. These issues are especially salient for ethnic minority adolescents because they remain at high risk for underutilization of substance abuse treatment compared to their White peers (Alegria, Carson, Goncalves, & Keefe, 2011).
As one of the largest ethnic minority groups, Latinos comprise more than 51 million people with the majority (65%) being of Mexican descent and a third of its population under the age of 18 (Pew Research Center, 2011). These adolescents report higher rates of substance use disorders (14%) compared to their White (12.7%) or African American (7%) peers as indicated by the Center on Addiction and Substance Abuse (2011). They are also more likely than White youth to be referred and mandated to attend substance abuse treatment from the criminal justice system (Shillington & Clapp, 2003). Latino adolescents, however, are less likely to complete substance abuse treatment compared to their White counterparts (Saloner, Carson, & Lê Cook, 2014). Thus, it is important to understand the factors that influence treatment retention and engagement for adolescents, in general, and Latino adolescents, in particular.
Precise definitions of treatment retention and engagement are difficult to operationalize because these variables are defined differently across studies and the format of treatment provided: residential, inpatient, or outpatient. In one of the more comprehensive studies of adolescent substance abuse treatment, Hser and colleagues (2001) examined data from a geographically diverse sample of almost 1,200 adolescents from four cities in the United States who participated in residential, acute inpatient or outpatient substance abuse treatment. Across these treatment modalities, marijuana and alcohol were the most frequently used substances, and about half the sample also reported some use of hard drugs (i.e., cocaine, hallucinogens, stimulants). Treatment retention was examined for each modality and defined as 90 days or more for residential and outpatient, and as 21 days for acute inpatient. Retention rates were highest for adolescents who received inpatient (63.7%) and residential (58.4%) treatment, and lowest for those in outpatient treatment (27.1%). After controlling for treatment type and baseline severity of drug use, Hser and colleagues found that longer time spent in treatment was associated with better overall outcomes for adolescents. This positive correlation between treatment retention and outcome is a robust finding that has been replicated in substance abuse treatment studies with adults and adolescents (Brady & Ashery, 2005; Garner et al., 2009; Greenfield et al., 2007; Simpson, Joe, & Brown, 1997). Unfortunately, Hser and colleagues found the lowest retention rates for the treatment modality that youth in the United States are most likely to receive, that is, outpatient treatment (SAMHSA, 2009).
Operational definitions of treatment engagement are also difficult to find in the literature because many studies do not provide clear definitions of engagement, or confound the definition of the construct with treatment attendance (see Pullmann et al., 2013; Staudt, 2007). However, some researchers suggest that measuring a behavior, such as treatment participation, is important when assessing engagement (see Joe, Simpson, & Broome, 1999; Staudt, 2007; Staudt, Lodato, & Hickman, 2012). For example, Stein and colleagues (2006) measured engagement in a sample of 130 incarcerated adolescents largely through assessing their participation in one of two assigned treatment conditions for substance abuse. Whereas agreed upon definitions across studies are lacking, there is support to suggest that attendance and participation are valid measures of treatment retention and engagement, respectively. Next, we examine the general factors that influence retention and engagement in substance abuse treatment for adolescents.
General Factors That Influence Retention and EngagementIn general, the severity of pretreatment substance use and the presence of an externalizing disorder are factors that have been found to influence the amount of time adolescents spend in treatment. In the adult literature, greater substance use severity at treatment admission is typically related to poorer treatment outcomes (SAMHSA, 2014a; Tiet, Ilgen, Byrnes, Harris, & Finney, 2007), but this is not consistently the case with adolescents. In other words, pretreatment substance use severity on its own does not consistently predict lower levels of treatment retention for adolescents (Latimer, Newcomb, Winters, & Stinchfield, 2000). Rather, it appears that pretreatment substance use severity in the presence of an externalizing disorder (i.e., attention deficit hyperactivity disorder, conduct disorder) is a stronger predictor of lower treatment retention for adolescents, especially in samples recruited from juvenile justice (see Austin & Wagner, 2010; Grella, Hser, Joshi, & Rounds-Bryant, 2001; Shane, Jasiukaitis, & Green, 2003). In fact, adolescents in the juvenile justice system tend to have higher rates of externalizing disorders, as well as substance use problems, compared to youth in the general population (Chassin, 2008; Rosenblatt, Rosenblatt, & Biggs, 2000). Most of the studies on treatment retention and engagement have largely been conducted with samples of White adolescents and excluded the investigation of cultural variables when diverse youth are included in the sample. In addition, more of a focus on juvenile justice is needed because this system serves as one of the primary referral sources for adolescents to substance abuse treatment (Ozechowski & Waldron, 2008). For example, data from the 2011 Treatment Episode Data Set for discharges (TEDS-D) indicates that approximately half of all youth, ages 12–20, discharged from publically funded substance abuse treatment were referred from the justice system (SAMHSA, 2014b). In sum, research that investigates how cultural variables influence treatment retention and engagement in juvenile justice involved ethnic minority youth is needed.
Ethnic Minority YouthAs previously mentioned, racial and ethnic minority youth are less likely to be retained in substance abuse treatment compared to their White counterparts (Jacobson, Robinson, & Bluthenthal, 2007; Saloner et al., 2014; Vourakis, 2005), although the reasons for these differences are not clear. Some have suggested that cultural variables play a role in substance abuse treatment retention and completion for ethnic minority youth (see Austin & Wagner, 2006; Castro & Alarcon, 2002), although heretofore the empirical evidence underlying this assumption has been lacking. For example, we were only able to locate two empirical studies, both by Austin and Wagner (2006, 2010), that directly examined the influence of cultural variables on treatment retention. In the 2010 study, the researchers’ investigated the influence of cultural and general variables on treatment attrition with a sample (N = 453) of Latino (domestic and foreign-born) and African American adolescents receiving substance abuse treatment. The adolescents in their sample received substance abuse treatment as part of their involvement with juvenile justice. Contrary to the researchers’ expectations, none of the cultural variables tested (i.e., acculturation, perceived discrimination, or racial/ethnic identity) influenced treatment completion, but rather some of the general variables were influential across racial and ethnic subgroups. For example, they found that not being placed on a waiting list and lack of a conduct disorder diagnosis influenced treatment completion for those Latino adolescents of U.S. and foreign birth, respectively. In light of the results from the Austin and Wager studies, the influence of cultural variables on substance abuse treatment retention and engagement has yet to be examined in adolescents of Mexican descent who represent the largest Latino subgroup.
Salient Cultural Variables for Latino YouthThree of the most salient cultural variables in relation to substance use and mental health for Latino adolescents include ethnic identity, familism, and acculturation (Castro & Alarcon, 2002; Umaña-Taylor & Updegraff, 2007; Vega & Gil, 1999). First, ethnic identity or a Latino adolescent’s sense of belonging to a particular ethnic group has been linked to mental health and substance use outcomes. For example, a stronger sense of ethnic identity is generally related to lower levels of psychological distress and substance use for Latino adolescents (Felix-Ortiz & Newcomb, 1995; Phinney & Ong, 2007; Umaña-Taylor, 2011). Second, familism is the sense of obligation and perceived support Latino adolescents experience within their families (Sabogal, Marin, Otero-Sabogal, Marin, & Perez-Stable, 1987). This cultural variable is relevant because Latino families with higher levels of familism do not generally condone the use of substances by its members (Vega, 1990). Finally, acculturation is considered a bidimensional process that involves the orientation Latino adolescents have toward being part of dominant and nondominant cultures simultaneously (Berry, 1980; Berry, Phinney, Sam, & Vedder, 2006). In general, the majority of research findings indicate a positive correlation between acculturation and rates of substance use for Latino adolescents (De La Rosa, Vega, & Radisch, 2000; Ebin et al., 2001; Lawton & Gerdes, 2014; Vega & Gil, 1999), although a few researchers have found a negative correlation, or no association, between these two variables (Miller, 2011; Zamboanga, Schwartz, Jarvis, & Van Tyne, 2009). In sum, all three of these cultural variables have been linked to substance use behavior and may assist in explaining treatment retention and engagement for Latino adolescents.
Purpose of Current Study and HypothesesThe purpose of the current study is to investigate the influence that cultural variables have in explaining treatment retention and engagement in sample of male Latino adolescents, primarily of Mexican descent, and largely recruited from juvenile justice. The study of Mexican American adolescent males with substance use problems involved in juvenile justice is a pressing need because they are overrepresented in this system (Mendel, 2011). For example, approximately 72% of the 1.5 million youth who have contact with the U.S. juvenile justice system each year are male (Puzzanchera, Adams, & Hockenberry, 2012) and it is estimated that 20% of these youth are Latino (Mendel, 2011). Further, more than half (56%) of the male youth in juvenile justice are estimated to have a substance use problem (Chassin, 2008). Our hypotheses are designed to test the influence that the cultural variables of ethnic identity, familism, and acculturation have on treatment retention and engagement. We include these specific cultural variables due to the links that have been identified with emotional/behavioral functioning and substance use behavior (Castro & Alarcon, 2002; Umaña-Taylor, 2011; Umaña-Taylor & Updegraff, 2007; Vega & Gil, 1999) for Latino youth. For the first hypothesis, we predict that cultural variables will influence treatment retention in the following ways: (a) higher levels of ethnic identity and familism will positively influence retention, whereas (b) higher levels of acculturation will negatively influence retention. Similarly, for the second hypothesis we predict that cultural variables will influence treatment engagement in the following ways: (a) higher levels of ethnic identity and familism will positively influence engagement, whereas (b) higher levels of acculturation will negatively influence engagement. The hypotheses are based on prior findings in the research literature that suggest higher levels of ethnic identity and familism serve as protective factors, whereas higher levels of acculturation serve as a risk factor in relation to substance use behavior for Latino adolescents (Lawton & Gerdes, 2014; Umaña-Taylor, 2011; Vega, 1990; Vega & Gil, 1999); we extrapolate these prior research findings to investigate their role in explaining treatment retention and engagement.
Method Description of Participants
Adolescents in this study (N = 96) were recruited as part of a larger set of studies examining the cultural accommodation of substance abuse treatment for Latino adolescents and randomly assigned to one of two group-based cognitive–behavioral treatment conditions for substance abuse (see Burrow-Sánchez, Minami, & Hops, 2015; Burrow-Sánchez & Wrona, 2012). The original data included nine females but information from these cases were dropped due to the limited ability to generalize from such a small sample of female adolescents, as well as the fact that male adolescents are overrepresented in juvenile justice (Mendel, 2011; Puzzanchera et al., 2012). Part of the inclusion criteria for the larger set of studies was that all adolescents were between the ages of 13–18, identified as Latino or Hispanic and met DSM–IV–TR (American Psychiatric Association, 2000) diagnostic criteria for a substance abuse or dependence disorder within the past 12 months. Adolescents were paid $20 via gift cards for completing the baseline assessments. Participants under the age of 18 were required to provide assent and parental consent prior to participation; all participant procedures for this study were approved by the Institutional Review Board at the University of Utah.
Description of Treatment
The two treatment conditions consisted of either a standard cognitive–behavioral treatment (S-CBT) or its culturally accommodated cognitive–behavioral (A-CBT) equivalent; in general, both treatments were similar except that the A-CBT integrated cultural variables relevant to Latino adolescents. Treatment groups met weekly for 90-min over 12-week periods. The reader is referred to our prior work (see Burrow-Sánchez, Martinez, Hops, & Wrona, 2011; Burrow-Sánchez, Minami, et al., 2015; Burrow-Sánchez & Wrona, 2012) for greater detail of the larger treatment studies, in general, and descriptions of the treatments, in particular.
Measures
All measures described below were available from their respective authors or publishers in English and Spanish and administered by trained bilingual research assistants. The majority of adolescents (98%) preferred completing the measures and verbal interactions with staff in English.
Timeline follow back (TLFB)
Substance use for all participants was measured using the TLFB (Sobell & Sobell, 1992), which is a semistructured interview that records substance use over a specified period of time. The TLFB uses a calendar format to help individuals remember their history and patterns of substance use. It has been used extensively with adolescents and appropriate psychometric properties have been established (Dennis, Funk, Godley, Godley, & Waldron, 2004; Sobell & Sobell, 2003). The number of days alcohol and other drugs (excluding tobacco) were used in the 90 days prior to baseline assessment was calculated for the analysis in the current study; to reduce skew, this variable was log-transformed prior to analysis.
Youth Self-Report–Externalizing Scale
The Youth Self-Report (YSR) is a well-used instrument with adolescent samples to measure behavioral problems across a number of domains (Achenbach, Dumenci, & Rescorla, 2002; Achenbach & Rescorla, 2001). The YSR consists of 112 items and participants are asked to rate their responses to potential behavioral problems on a 3-point Likert-type scale that ranges from 0 (not true) to 2 (very true or often true) based on the past 6 months. From the complete YSR measure nine subscales and three overall scales can be derived. Although the complete measure was administered to adolescents, only the Externalizing scale (EXT) was used in the analysis for the present study. Examples of items from the Externalizing scale include “I drink alcohol without my parents’ approval” and “I destroy my own things.” Internal consistency for adolescents on the EXT scale was α = .868.
The Multi Ethnic Identity Measure
The Multi Ethnic Identity Measure (MEIM) is a widely used measure of ethnic identity for adolescents (Phinney, 1992; Phinney & Ong, 2007). A modified 12-item version of the MEIM was used in this study that has been tested via confirmatory factor analysis with Latino adolescents (see Burrow-Sánchez, 2014). The items asked adolescent participants to indicate their attitudes and behaviors related to their ethnic identity group on a 1 (disagree) to 5 (agree) scale. Contemporary views of ethnic identity consider it to be a bidimensional construct, and Phinney and Ong (2007) suggested that the measure be scored by reducing it to two 3-item subscales: a Commitment (COM; Items 6, 7, and 12) scale that measures a sense of personal affiliation to an ethnic group and an Exploration (EXP; Items 1, 2, and 8) scale that measures behavior related to seeking information about an ethnic group. An example of an item from the COM scale is “I feel I identify with the ethnic Group 1 belong to,” and an example of an item from the EXP scale is “I have dedicated time to find out more about my ethnic group, such as history, tradition, and customs.” Following the suggestion provided by Phinney and Ong scores from six items were extracted from the larger measure and then averaged to produce two 3-item scores for each participant. Internal consistency for the exploration and commitment subscales in the current study was α = .730 and α = .778, respectively.
Familism Scale
The Familism Scale (FS) is a 14-item instrument used to measure the construct of familism based on the factors of obligations, perceived support, and family as referents (Sabogal et al., 1987). Versions of this scale have been used in prior research with Latino youth (see Lorenzo-Blanco, Unger, Ritt-Olson, Soto, & Baezconde-Garbanati, 2013; Morcillo et al., 2011; Unger et al., 2002). Participants rate their agreement to items on a scale ranging from 1 (very much in disagreement) to 5 (very much in agreement). Examples of items from the scale include, “Children should live in their parents’ home until they get married” and “A person should share his or her home with uncles, aunts, or first cousins if they are in need.” Scores from individual items were averaged to obtain a total score. Internal consistency for this sample was α = .820.
Acculturation Rating Scale for Mexican Americans-II
The Acculturation Rating Scale for Mexican Americans-II (ARSMA-II) is one of the most widely used measures of acculturation for Latino adults and adolescents (Cuéllar, Arnold, & Maldonado, 1995). It has demonstrated good reliability and strong construct and discriminant validity in research with Mexican American samples (Cuéllar et al., 1995) and has been tested via confirmatory factor analysis for Latino adolescents with substance use disorders (see Burrow-Sánchez, Ortiz-Jensen, Corrales, & Meyers, 2015). Participants rate their responses to items on a scale ranging from 1 (not at all) to 5 (extremely often or always). The 13-item Anglo Oriented Subscale (AOS) and the 17-item Mexican Oriented Subscale (MOS) were scored separately for participants by averaging the items on each subscale in accordance with Cuéllar et al. (1995) and a bidimensional view of acculturation (Berry, 2006). Examples of items from each scale include “I have difficulty accepting some ideas held by some Mexican Americans” (AOS) and “My friends, while I was growing up, were of Mexican origin” (MOS). Internal consistency was α = .707 and α = .850 for AOS and MOS subscales, respectively. Cuéllar et al. (1995) also provides a linear method for calculating an overall acculturation score by subtracting participant mean scores of the AOS from the MOS; the participants’ acculturation score is then used to place them in one of five categories across a continuum that ranges from less acculturated on one end to more highly acculturated on the other with those more toward the center labeled as bicultural. For the sake of parsimony, we chose to collapse the five categories into three which resulted in placing the sample in the following categories: 39% were more Mexican oriented or less acculturated, 48% were bicultural, and 14% were more Anglo oriented or highly acculturated.
Retention: Number of sessions attended
Retention was measured by the total number of treatment sessions attended by each adolescent. A session was considered to be attended if the adolescent was present for the majority of the 90-min treatment session, typically 75-min or more. Attendance was closely monitored and recorded by therapists at the end of each session. Participant attendance was also discussed as part of the weekly supervision provided to therapists. The mean (SD) and median number of treatment sessions attended were 8.95 (3.16) and 10, respectively; range = 0–12.
Engagement: Number of practice sheets completed
Engagement was measured by the total number of practice sheets completed by each adolescent over the course of treatment. Practice sheets were administered to adolescents by therapists at 11 of the 12 treatment sessions. The sheets provided adolescents with an opportunity to practice treatment-related skills between sessions and then report on their progress at the next session; successful completion of practice sheets required the adolescents’ attention to the in-session treatment content as well as application of material outside of session. The completion of practice sheets was used as a behavioral indicator of adolescent participation in treatment. Practice sheets were provided to adolescents on standard 8.5 × 11 in. sheets of paper that could be completed with a pen or pencil. A practice sheet was considered complete if the therapist judged that the majority of it, typically 75% or more, had reasonably been completed by an adolescent. Therapists also judged the quality of work on practice sheets compared to that typically expected given a particular adolescents’ chronological age and, if known, reading ability. Practice sheet completion was also discussed as part of the weekly supervision provided to therapists. The mean (SD) and median number of practice sheets completed were 5.08 (3.11) and 5, respectively; range = 0–11.
Analytical Plan
The dependent measures in this study (i.e., total number of sessions attended and number of practice sheets completed) are count variables. These types of variables generally follow a Poisson, rather than a normal, distribution and are most appropriately analyzed using generalized linear methods (see Coxe, West, & Aiken, 2009). The independent variables in this study are covariates (i.e., baseline substance use and externalizing behavior) and baseline cultural variables of interest (i.e., ethnic identity, familism, acculturation). More specifically, the following predictors (in order and coded by measure acronym): treatment condition (TXC), age, TLFB, EXT, EXP, COM, FS, AOS, and MOS were used in the regressions. Controlling for baseline substance use and externalizing behavior in the generalized regression models allowed us test for the variance in outcomes attributed to the cultural variables of interest. We grand mean centered all predictors with the exception of TXC to ease interpretation of the models (Hedeker & Gibbons, 2006); TXC was included as a covariate in all models to control for any influence that assignment to a specific treatment condition had on participant outcomes. Two generalized linear regressions were conducted to test each dependent variable (i.e., retention or engagement) but the same independent variables were included in both models. Further, the subscales for ethnic identity and acculturation were included in the models so that these constructs could be tested bidimensionally; this type of bidimensional analysis is frequently suggested in the literature but infrequently conducted in practice (see Burrow-Sánchez, Ortiz-Jensen, et al., 2015; Phinney & Ong, 2007).
Results Preliminary Analyses
The mean age of adolescents in the analysis was 15.29 (SD = 1.31) and the majority had parents of Mexican descent (77%; see Table 1 for more participant demographics). The majority of study referrals was received from juvenile justice probation officers (68%) or case managers (30%) and was mandated (69%) to attend substance abuse treatment. Finally, 55% and 45% of adolescents met DSM-IV–R criteria for a substance abuse or dependence disorder, respectively, within the past 12 months. See Table 1 for additional participant demographics.
Participant Demographics (N = 96)
Predictors of Retention and Engagement
Inspection of the means (8.73 and 4.92) and variances (10.66 and 9.61) for retention and engagement, respectively, indicated they were not equal and that dispersion was present (see Stroup, 2013). Minor violations of the mean-variance equality assumption (i.e., low dispersion) can be addressed with a Poisson model that includes a dispersion correction factor but larger violations generally require the use of a negative binomial model. Following this logic, we conducted a Poisson regression for the retention dependent variable (DV) and a negative binomial regression with the engagement DV.
The first model included retention as the DV and the fit statistics indicated a Pearson χ2 of 95.06 (df = 86, Pearson χ2/df = 1.11); the Pearson value over its degrees of freedom is an indicator of overall model fit with ratios closer to 1 reflecting a perfect fit. To account for minor dispersion in the DV, we included a Pearson correction factor in the model that adjusts standard errors. Results of the analysis are presented in Table 2 and indicate that EXP (β = 0.04, p = .03), and FS (β = 0.01, p = .0007) significantly predicted retention as did Anglo orientation (β = −0.20, p = .006) but in the opposite direction. Poisson models produce coefficients that are interpreted as the predicted logarithm of counts of the DV (see Coxe et al., 2009). For example, coefficients for the first model represent the predicted change in the logarithm of counts for retention for a one-unit change in the predictor; however, exponentiation of the coefficients places them on the scale of the original count variable (i.e., number of sessions attended) and subsequently eases interpretation. Therefore, the exponentiated coefficients (see Table 2) are used for the interpretation of all model results. The value of the intercept in model predicts that participants will attend 8.03 sessions when all other terms are zero. However, for Poisson-based analysis, the remaining terms in the model represent a multiplicative change in the DV for a one-unit change in the predictor (see Coxe et al., 2009). Applied to Model 1, this indicates that for every one-unit change in EXP the number of sessions attended is multiplied by a factor of 1.04; thus, for every one-unit change in EXP participants are predicted to attend 8.35 (i.e., 8.03 × 1.04 = 8.35) treatment sessions. Similarly, for every 1-unit change in FS participants are predicted to attend 8.11 (i.e., 8.03 × 1.01 = 8.12) treatment sessions. In contrast, a one-unit change in AOS predicts that participants will attend 6.58 (i.e., 8.03 × 0.82 = 6.58) treatment sessions.
Poisson Model for Treatment Retention
The second model included the same predictors as the first, but the DV was changed to engagement and a negative binomial regression was used due to the rationale presented above. The second model produced a Pearson χ2 of 89.79 (df = 86, Pearson χ2/df = 1.04). The exponentiated value of the intercept (see Table 3) indicates that participants are predicted to complete 4.36 practice sheets when all other terms are zero. For every one-unit change in EXP participants are predicted to complete 4.71 practice sheets (i.e., 4.36 × 1.08 = 4.71). In contrast, for 1-unit changes in TLFB and AOS participants are predicted to complete 3.4 (i.e., 4.36 × 0.78 = 3.4) and 3.1 (i.e., 4.36 × 0.71 = 3.1) practice sheets, respectively.
Negative Binomial Model for Treatment Engagement
DiscussionThe goal of the current study was to investigate how cultural variables influence retention and engagement in substance abuse treatment for a sample of Latino adolescents primarily of Mexican descent and largely recruited from juvenile justice. Overall, the results indicated that adolescents who were in the exploration phase of ethnic identity and reported a stronger sense of familism had higher rates of retention and engagement in substance abuse treatment. In contrast, adolescents who reported higher levels of acculturation to the dominant culture (i.e., Anglo orientation) had lower rates of retention and engagement in treatment.
The exploration component of ethnic identity, rather than the commitment component, positively influenced treatment retention and engagement. These findings may be due, in part, to the fact that adolescence is a critical period of development when youth are faced with many tasks and challenges that encourage exploration of their identities. However, youth from ethnic minority backgrounds must accomplish the typical developmental tasks of adolescence while concurrently exploring their sense of self as a member of a nondominant group (Erikson, 1997; Umaña-Taylor & Updegraff, 2007). In other words, adolescence is a key time for ethnic minority youth to seek out information regarding their identity as a person and as a member of an ethnic group. The treatment groups attended by adolescents in the current study were entirely composed of youth who identified as Latino/Hispanic and largely of Mexican descent which may have served to provide safe venues for exploring ethnic identity, which subsequently promoted higher retention and engagement. In contrast, it may not be reasonable to expect that Latino youth in middle adolescence (i.e., ages 14 to 16) have yet developed a strong commitment to their ethnic identity because exploration may be more salient during this developmental period (Berry et al., 2006).
Familism also positively influenced treatment retention and engagement for adolescents in our sample. These findings may suggest that Latino families who continue to exert a positive influence on their children during adolescence may buffer some of the negative effects attributed to peers (Dishion & Owen, 2002; Duncan, Duncan, & Hops, 1994; Umaña-Taylor & Guimond, 2010). More specifically, families with higher levels of familism may view drug use by its members from a collective prospective, rather than viewing it as an individual problem (Vega, 1990; Vega & Gil, 1999). Following this logic, the family system may exert pressure and provide support for its adolescent members to resolve the drug problem (i.e., attend and engage in treatment) because it viewed as affecting the entire family.
In contrast to findings just described, higher levels of affiliation to the dominant culture (i.e., Anglo orientation) negatively influenced treatment retention and engagement for adolescents in the study whereas affiliation to the Mexican culture was unrelated to retention or engagement. These findings may suggest that Mexican American adolescents with more affiliation toward the dominant culture experience less connection and cohesion from participating in treatment groups with their less acculturated peers. The level of cohesion is an important element to consider in group treatment participation and outcomes (see Burlingame, McClendon, & Alonso, 2011), and subsequently, less connection with peers could lead to lower motivation to attend and engage in treatment. Based on the combined ARSMA-II scores slightly more than 60% of the sample were in bicultural (48%) or highly acculturated (14%) categories compared to 39% in the less acculturated category. These proportions suggest that, overall, the sample was more highly acculturated which could help to explain the lack of findings for the Mexican orientation scale.
Finally, we found that higher baseline levels of substance use negatively influenced treatment engagement for adolescents in the study. This finding is partially consistent with other research (see Grella et al., 2001; Shane et al., 2003), and may suggest that adolescents with severe pretreatment substance use problems require more intensive engagement strategies than standard outpatient treatments typically provide (see Ozechowski & Waldron, 2008). This is important to consider with the fact that adolescents are most likely to receive outpatient treatment for substance abuse problems (SAMHSA, 2009). We also found that externalizing behavior did not predict either treatment retention or engagement for adolescents. This may be due to the fact that the adolescents in our study were not formally diagnosed with an externalizing disorder, and subsequently, their externalizing behaviors may not have reached a threshold to influence retention and engagement similar to other studies (Austin & Wagner, 2010; Grella et al., 2001; Shane et al., 2003).
Findings from the current study suggest two important clinical implications that we encourage practitioners to consider. First, we found that specific cultural variables do indeed influence treatment retention and engagement for Latino adolescents. Based on these findings, practitioners may want to consider measuring cultural variables for Latino adolescents as part of a pretreatment assessment. For example, scores on measures of cultural variables, such as ethnic identity and acculturation, may assist practitioners in assigning adolescents to treatment groups with peers who share similar cultural perspectives. Second, the study findings suggest that placing adolescents together in groups who all share common identity labels, such as Latino or Hispanic, may not always be the best approach. This second implication underscores the point that differences in perspective can exist even when adolescents share a common racial or ethnic identity label. For example, a practitioner cannot assume that two adolescents who both identify as Latino share the same perspective, orientation or world-view toward their culture of origin or the host culture. It may be that ethnic minority adolescents benefit more from being in treatment groups with peers who share similar cultural perspectives rather than similar identity labels.
As with any study there are certain limitations that need to be considered. First, the sample size in this study was modest, which limits the statistical power we had to detect the influence of cultural variables, and thus, recommend that future research be conducted with larger samples of Latino adolescents. Second, adolescents in this study were primarily of Mexican descent, male, and juvenile justice involved which limits generalizability to other Latino subgroups (e.g., Puerto Rican, Cuban) and females who do not have contact with the justice system. Future research that includes females and samples from other Latino subgroups that are not justice-involved is needed to replicate the current findings. In addition, future research that examines the influence of cultural variables on treatment outcomes for other racial and ethnic minority adolescent groups (e.g., African American, American Indian) is needed. Finally, this study focused on group-based outpatient treatment and other studies should be conducted that investigate the influence of cultural variables for other treatment modalities (i.e., individual, family) and settings (i.e., residential, inpatient) for racial and ethnic minority adolescents.
The goal of the current study was to investigate the influence of specific cultural variables on treatment retention and engagement in sample of male Latino adolescents, primarily of Mexican descent, and largely recruited from juvenile justice. The major results of the study indicated that adolescents in the exploration phase of ethnic identity, and reporting higher levels of familism, had higher retention and were more engaged in treatment. In contrast, adolescents who reported more orientation toward the dominant culture (i.e., Anglo orientation) had lower retention and were less engaged. Clinical implications of these findings suggest that practitioners may want to consider cultural perspectives in addition to identity labels (e.g., Latino, Hispanic) when assigning Latino adolescents to group treatments. Future research should replicate the findings in the current study with larger samples of adolescents from both genders and across other Latino subgroups (e.g., Puerto Rican, Cuban).
Footnotes 1 Both studies used portions of the same sample and we only report on the most recent study; however, findings from both studies did not indicate a relation between cultural variables and treatment attrition/retention.
2 The authors did not explicitly indicate in either study the Latino subgroup composition of their samples. Since the samples were recruited in the Southwest portion of the U.S. (i.e., FL) it is assumed they were mostly of Puerto Rican or Cuban descent.
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Submitted: January 5, 2015 Revised: May 1, 2015 Accepted: May 1, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 969-977)
Accession Number: 2015-31332-001
Digital Object Identifier: 10.1037/adb0000096
Record: 65- The influence of impulsiveness on binge eating and problem gambling: A prospective study of gender differences in Canadian adults. Farstad, Sarah M.; von Ranson, Kristin M.; Hodgins, David C.; El-Guebaly, Nady; Casey, David M.; Schopflocher, Don P.; Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015 pp. 805-812. Publisher: American Psychological Association; [Journal Article] Abstract: This study investigated the degree to which facets of impulsiveness predicted future binge eating and problem gambling, 2 theorized forms of behavioral addiction. Participants were 596 women and 406 men from 4 age cohorts randomly recruited from a Canadian province. Participants completed self-report measures of 3 facets of impulsiveness (negative urgency, sensation seeking, lack of persistence), binge-eating frequency, and problem-gambling symptoms. Impulsiveness was assessed at baseline, and assessments of binge eating and problem gambling were followed up after 3 years. Weighted data were analyzed using zero-inflated negative binomial and Poisson regression models. We found evidence of transdiagnostic and disorder-specific predictors of binge eating and problem gambling. Negative urgency emerged as a common predictor of binge eating and problem gambling among women and men. There were disorder-specific personality traits identified among men only: High lack-of-persistence scores predicted binge eating and high sensation-seeking scores predicted problem gambling. Among women, younger age predicted binge eating and older age predicted problem gambling. Thus, there are gender differences in facets of impulsiveness that longitudinally predict binge eating and problem gambling, suggesting that treatments for these behaviors should consider gender-specific personality and demographic traits in addition to the common personality trait of negative urgency. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Influence of Impulsiveness on Binge Eating and Problem Gambling: A Prospective Study of Gender Differences in Canadian Adults
By: Sarah M. Farstad
Department of Psychology, University of Calgary
Kristin M. von Ranson
Department of Psychology, University of Calgary;
David C. Hodgins
Department of Psychology, University of Calgary
Nady El-Guebaly
Division of Addiction, Foothills Addiction Program, Department of Psychiatry, University of Calgary
David M. Casey
Department of Psychology, University of Calgary
Don P. Schopflocher
School of Public Health and Faculty of Nursing, University of Alberta
Acknowledgement: David M. Casey is now at Clinical Quality Metrics, Alberta Health Services, Calgary, Alberta.
This research was supported by awards to the first author from the Social Sciences and Humanities Research Council of Canada and the Alberta Gambling Research Institute and a fellowship from Intersections of Mental Health Perspectives in Addictions Research Training. This article is based on the master’s thesis research of Sarah M. Farstad, which was completed under the supervision of Kristin M. von Ranson. We thank Dr. Thomas O’Neill and Dr. Jenny Godley for their feedback on previous drafts of the manuscript. We thank the Leisure, Lifestyle, Lifecycle Project participants, and acknowledge the use of the project’s data in this article.
There has been considerable debate over whether binge eating should be viewed as addictive. Binge eating and addictions share many similarities: Both involve a lack of control over behavior, are used to regulate emotions, and increase dopamine activity in the brain’s limbic system (Davis & Carter, 2009; Wilson, 2000). Despite these similarities, some have argued that the addiction model ignores critical aspects of eating disorders (i.e., body-image disturbances, weight/shape preoccupation) and fails to account for the differences in symptoms among eating disorders (von Ranson & Cassin, 2007; Wilson, 2000, 2010). Further research comparing binge eating to other addictions is needed to determine the appropriateness of conceptualizing binge eating as an addictive behavior.
Elevated rates of eating disorders have been found among problem gamblers (von Ranson, Wallace, Holub, & Hodgins, 2013) and elevated problem-gambling symptoms have been found among those with binge eating disorder (Jiménez-Murcía et al., 2013; Yip, White, Grilo, & Potenza, 2011), but not bulimia nervosa (Fernandez-Aranda et al., 2006; Jiménez-Murcía et al., 2013). Identifying shared personality traits associated with binge eating and problem gambling may uncover a common vulnerability that places individuals at risk of developing these disorders, whereas identifying disorder-specific personality traits may allow us to better understand why individuals engage in one behavior over another (i.e., binge eating or gambling). Accordingly, the focus of this study was to compare key personality traits associated with both binge eating and problem gambling.
In this paper, we focused on impulsiveness, which is an important personality trait associated with addiction (Verdejo-Garcia, Lawrence, & Clark, 2008). Impulsiveness is a multifaceted construct composed of at least five distinct facets: negative urgency (i.e., tendency to engage in impulsive behavior when experiencing strong negative emotions), positive urgency (i.e., tendency to engage in impulsive behavior when experiencing strong positive emotions), sensation seeking (i.e., a desire for thrills and excitement), lack of planning (i.e., inability to consider the consequences of one’s actions), and lack of persistence (i.e., inability to persist on tasks when bored or fatigued) (Whiteside & Lynam, 2001). Each facet of impulsiveness is associated with different types of behavior (e.g., Fischer & Smith, 2008), underscoring the need to distinguish among impulsiveness facets in studies of problematic behaviors.
Impulsiveness, Binge Eating, and Problem GamblingDepending on the sample, study design, and form of psychopathology, study results regarding the relationship of impulsiveness with binge eating and problem gambling have been mixed. Negative urgency has been a robust correlate of binge eating and problem gambling in undergraduate, clinical, and community samples (Fischer, Peterson, & McCarthy, 2013; Fischer & Smith, 2008; Fischer, Smith, & Cyders, 2008; MacLaren, Fugelsang, Harrigan, & Dixon, 2011). However, in the few longitudinal studies of undergraduates that have been conducted to date, negative urgency has not been a significant predictor of future binge eating or gambling (Cyders & Smith, 2008; Davis & Fischer, 2013; Peterson & Fischer, 2012).
In community and undergraduate samples, sensation seeking has not been significantly associated with binge eating or problem gambling (Fischer & Smith, 2008; Reid et al., 2011). In clinical samples, some studies have found a small, positive association between sensation seeking and problem gambling (Grall-Bronnec et al., 2012), whereas others have not (Albein-Urios, Martinez-González, Lozano, Clark, & Verdejo-García, 2012). In longitudinal studies using undergraduate samples, sensation seeking has not significantly predicted future binge eating or gambling (Cyders & Smith, 2008; Peterson & Fischer, 2012).
Cross-sectional studies using clinical, community, and university samples have consistently found that lack of persistence is unassociated with binge eating and bulimic symptoms (Peterson & Fischer, 2012); however, results regarding problem gambling have been mixed. In community samples, individuals with problem gambling had elevated lack-of-persistence scores relative to controls (e.g., Reid et al., 2011), but in university samples, problem gambling has been unassociated with lack of persistence (e.g., Fischer & Smith, 2008). In clinical samples, some studies have revealed a positive association between lack of persistence and problem gambling (Grall-Bronnec et al., 2012), whereas others have not (Albein-Urios et al., 2012). In longitudinal studies of undergraduates, lack of persistence significantly predicted future binge eating, but not gambling (Cyders & Smith, 2008; Peterson & Fischer, 2012).
Together these findings suggest that in community samples, negative urgency is concurrently associated with both binge eating and problem gambling, lack of persistence is concurrently associated with problem gambling only, and sensation seeking is not associated with either behavior. In longitudinal studies of undergraduate students, lack of persistence appears to predict future binge eating but not problem gambling, and negative urgency and sensation seeking do not appear to predict either behavior. It is likely that results from previous studies have varied because the studies examined different samples (i.e., undergraduate, community, clinical) and were composed of different proportions of women and men, different age ranges, and varying levels of disorder severity.
Present StudyThe aim of this study was to examine gender differences in the longitudinal personality predictors of binge eating and problem gambling in a large population-based sample of women and men using a sophisticated statistical approach. In this study we intended to address several limitations associated with existing research. One of the largest gaps in the existing literature on the association of impulsiveness with binge eating and problem gambling is the dearth of research on gender differences. Identifying gender differences in the predictors of these disorders could help explain the mixed results we have observed, and could help us develop more effective, tailored interventions by targeting traits that are most problematic for each gender.
A second limitation of the existing literature is that it is largely based on clinical and undergraduate samples, which may differ from the general population. Treatment-seeking individuals often have higher rates of psychiatric comorbidity and greater disorder severity than community dwellers (Berkson, 1946) and university samples tend to be unrepresentative of the general population (Henrich, Heine, & Norenzayan, 2010). Thus there is a need for greater reliance on community studies, especially those that are representative of the population at large.
A third limitation of the existing research is the lack of longitudinal studies. Cross-sectional studies do not allow us to determine whether impulsiveness predicts future binge eating and problem gambling or whether it simply co-occurs with these behaviors. To draw conclusions about causation or order effects, prospective studies are needed (Kraemer et al., 1997). The longitudinal studies conducted to date have exclusively used undergraduate samples with a restricted age range; it is important to ascertain whether their findings can be generalized to community samples of adults of varying ages.
Finally, the statistical approaches used in most existing studies have provided limited information. Zero-inflated regression analysis is a sophisticated statistical approach that operates under the assumption that different processes may be involved when predicting who engages in a given behavior versus who has more severe symptoms of the behavior among those who do engage in the behavior. Thus, this approach may provide a fine-grained understanding of the relationship between impulsiveness and both binge eating and problem gambling.
We posed three hypotheses: (1) Negative urgency would predict future binge eating and problem gambling; (2) lack of persistence would predict future binge eating and problem gambling; and (3) sensation seeking would not significantly predict either behavior. Although little research on gender differences exists, different subtypes of individuals with problem gambling appear to be characterized by different impulsivity profiles: Women tend to have higher rates of mood and anxiety disorders and tend to use gambling as a means of affect regulation, whereas men are more likely to be characterized by attentional problems, impulsivity, and risk taking (Blaszczynski & Nower, 2002). Based on previous research and the pathways model of problem gambling (Blaszczynski & Nower, 2002), we predicted that negative urgency would be a more prominent predictor of binge eating and problem gambling for women because it is tied to difficulties coping with strong negative emotions, and lack of persistence would be a more prominent predictor of these behaviors for men because it is tied to difficulties coping with boredom. Although the pathways model would predict increased sensation seeking among men with problem gambling, the existing literature does not support that prediction in community samples, so we expected that it would not be a prominent predictor for men in our study.
MethodOur sample was drawn from the Leisure, Lifestyle, Lifecycle Project (LLLP), a longitudinal cohort study among Albertans (for details, see El-Guebaly et al., 2008). The research protocol was approved by ethics committees at the Universities of Calgary, Alberta, and Lethbridge and all participants provided informed consent.
Leisure, Lifestyle, and Lifecycle Project
The LLLP study recruited 1284 participants from the general population and 524 at-risk gamblers who scored at or above the 70th percentile on gambling frequency or expenditure. Although the goal was to have participants divided equally between both genders and among five age groups (13–15, 18–20, 23–25, 43–45, 63–65), the 13–15-year-old (24%) and 43–45-year-old (22%) age groups were slightly overrepresented and there were slightly more female participants (53%) than male. Nevertheless, the sample largely reflected the Alberta population.
Eligibility criteria included living in Alberta for at least 3 months, age in one of five prespecified age cohorts, and a minimum fifth-grade reading level. Participants were excluded if they showed evidence of uncontrolled psychosis. At intake, participants completed a 45-min telephone interview and a 3-hr computer-based survey and face-to-face interview. Each participant was then contacted every 14 to 18 months for a total of four rounds (“waves”) of data collection. Data collection in succeeding waves was collected through an online survey or if requested, a paper copy was mailed along with a prepaid envelope to return the survey. The current study used self-report data obtained at Waves 1 and 3. The personality measure was completed at Wave 1 and the eating and gambling measures were completed at Wave 3, which occurred approximately 3 years later. Participants were paid $75 after completing Wave 1 and $45 after completing Wave 3.
Sample Characteristics/Participants
A total of 1372 adults completed Wave 1 and 1002 completed Wave 3, resulting in a 73% retention rate. A higher proportion of participants completing both assessments were female (OR = 1.65, 95% CI: 1.30–2.10), married (OR = 1.46, 95% CI: 1.15–1.87), older (OR = 1.93, 95% CI: 1.52–2.46), and more highly educated (OR = 2.58, 95% CI: 1.84–3.62). There were no differences between the two groups in employment status or household income. Participants completing both assessments endorsed fewer problem-gambling symptoms (d = .20, 95% CI: .07–.34) and had lower scores on negative urgency (d = .20, 95% CI: .08–.31), sensation seeking (d = .39, 95% CI: .27–.51), and lack of persistence (d = .12, 95% CI: .00–.24) than those completing Wave 1 alone. Although these differences generally corresponded to small to medium effect sizes , they suggest our sample was skewed toward older, married, and well-educated individuals.
The sample for this study included 1002 participants (596 women) who completed both Waves 1 and 3 of the LLLP study. Participants were drawn from the four adult age cohorts: 18–20 (n = 197), 23–25 (n = 238), 43–45 (n = 311), and 63–65 years old (n = 256). The majority of the sample was Caucasian (91.8%), had completed at least some college or university (84.3%), were employed full-time (49.4%) or part-time (19.6%), and had a household income greater than $60,000 (59.5%).
Measures
Selected questions from NEO-PI-R and NEO Five Factor Inventory (NEO-FFI-3)
Participants completed short versions of each domain of personality and an in-depth assessment of Neuroticism and Extraversion from the NEO-PI-R and NEO-FFI-3 (Costa & McCrae, 1992). Each item is rated on a 5-point scale. Four facets of impulsiveness (excluding positive urgency) can be assessed using the NEO-PI-R (Whiteside & Lynam, 2001). Scores on the impulsiveness facet of Neuroticism assessed negative urgency, scores on the excitement-seeking facet of Extraversion assessed sensation seeking, and scores on the self-discipline facet of Conscientiousness assessed lack of persistence. In the current study, we analyzed archival data which did not assess the fourth type of impulsiveness assessed by the NEO-PI-R, so we were unable to study lack of planning. Two impulsiveness items that specifically referred to overeating were removed to avoid redundancy in predicting binge eating. Scores on self-discipline were reverse-coded so that higher scores reflected higher levels of impulsiveness. Coefficient alphas for impulsiveness, excitement seeking, and self discipline were .72, .72, and .83.
Eating Disorder Examination–Questionnaire
The EDE-Q 6.0 (Fairburn, 2008; Fairburn & Beglin, 1994) is a 33-item self-report measure that assesses eating-disorder symptoms, including binge eating, in the past 28 days. Each item is rated on a 7-point scale. Coefficient alphas of the Global, Restraint, Eating Concern, Weight Concern, and Shape Concern subscales were .91, .79, .82, .84, and .91.
Problem Gambling Severity Index
The PGSI (Ferris & Wynne, 2001) is a 9-item measure that assesses problem-gambling symptoms in the past 12 months. Each item is rated on a 4-point scale. In this study, a problem-gambling symptom was coded as “present” if the individual reported experiencing the symptom sometimes, most of the time, or almost always and it was coded as “absent” if the individual reported never experiencing the problem-gambling symptom. Coefficient alpha for the PGSI was .84.
Statistical Analyses
In part because the complex sampling design tapped only certain age groups and oversampled at-risk gamblers, survey weights taking into account age, sex, geographical location, and the oversampling of at-risk gamblers were applied using data obtained from the Alberta Ministry of Health and Wellness to ensure the results more accurately reflected the entire population of Alberta adults.
Zero-inflated negative binomial (ZINB) and zero-inflated Poisson (ZIP) regression models were estimated using Mplus Version 7 (Muthén & Muthén, 1998–2012). These analyses are appropriate when the dependent variable is a count variable and when there is a large percentage of zero values (Hilbe, 2011). The independent variables were age, negative urgency, sensation seeking, and lack of persistence assessed at Wave 1. Age was included in each model because it is known to be associated with both binge eating and problem gambling (American Psychiatric Association, 2013). The dependent variables were the number of objective binge episodes in the past 28 days and the number of problem-gambling symptoms endorsed in the past 12 months assessed at Wave 3. Note that because continuous dependent variables are inappropriate for use with negative binomial and Poisson regression models, we did not use the total score of the PGSI as an outcome variable (Hilbe, 2011).
For each analysis in ZINB and ZIP regression, two models are estimated simultaneously: a logistic component and a count component. The logistic component estimates the probability of not engaging in the behavior and the count component estimates the association between the independent and dependent variables among those people who engage in the behavior (Atkins & Gallop, 2007; Hilbe, 2011). To simplify interpretation, odds ratios were inverted so that higher values indicated higher likelihood of engaging in binge eating or endorsing problem-gambling symptoms. Both models were estimated using maximum-likelihood estimation with robust standard errors.
The choice to use ZINB or ZIP models was made based on the significance of the dispersion statistic (Atkins & Gallop, 2007). Among women, the ZINB model was used for predicting the number of objective binge episodes and the ZIP model was used to predict the number of problem-gambling symptoms. Among men, the ZIP model was used to predict the number of problem-gambling symptoms. The logistic component of the ZINB model for objective binge episodes did not converge among males, so we used noninflated negative binomial models, which only predict the count component of the model. All analyses were run using the entire sample, and again after excluding those individuals who reported both binge eating and problem-gambling symptoms (BE + PG). The purpose of the latter analyses was to examine the degree to which results regarding binge eating and problem gambling were influenced by comorbid symptoms of the other behavior.
Results Descriptive Statistics
Among those who completed the binge-eating assessment at Wave 3, approximately one quarter of women and 10% of men reported past-month binge episodes (see Table 1). Among those who completed the problem-gambling assessment at Wave 3, approximately one quarter of women and one third of men reported past-year problem-gambling symptoms. Fifty-two women (9.1%) and 21 men (5.3%) reported comorbid binge eating and problem-gambling symptoms. Using Spearman’s rho, binge eating and problem gambling were positively associated with negative urgency and lack of persistence among women and men (see Table 2). Among men only, problem gambling was positively associated with sensation seeking. Among women, binge eating and problem gambling were negatively associated with age. Binge eating and problem gambling were positively correlated among women and men. These effects were small to medium.
Descriptive Statistics for Binge-Eating Episodes and Problem-Gambling Symptoms Divided by Gender
Intercorrelations of Age, Impulsiveness, Binge-Eating and Gambling Variables Divided by Gender
Analyses Using the Total Sample
Binge eating among women
Age and negative urgency at Wave 1 predicted the presence of binge eating at Wave 3 (see Table 3). Every 1-year increase in age decreased the odds of binge eating by 4%, whereas a one-unit increase in negative urgency increased the odds of binge eating by 21%. There were no significant predictors of increased severity among those who reported engaging in binge eating.
Zero-Inflated Negative Binomial Regression Models of Objective Binge Episode Frequency Among Women and Men
Binge eating among men
Age, negative urgency, and lack of persistence at Wave 1 were significantly associated with severity of binge eating at Wave 3 (see Table 3). A 1-year increase in age was associated with a 5% increase in reported binge episodes. A one-unit increase in negative urgency was associated with a 22% increase in the number of reported binge episodes and a one-unit increase in lack of persistence was associated with a 20% increase in the number of reported binge episodes.
Problem gambling among women
Negative urgency at Wave 1 predicted the presence of problem-gambling symptoms at Wave 3 (see Table 4). Every one-unit increase in negative urgency increased the odds of endorsing problem-gambling symptoms by 12%. Only age at Wave 1 predicted increased severity among those who reported problem-gambling symptoms at Wave 3: A 1-year increase in age was associated with a 2% increase in the number of reported problem-gambling symptoms.
Zero-Inflated Poisson Regression Models of Problem Gambling Among Women and Men
Problem gambling among men
Sensation seeking at Wave 1 was the only significant predictor of problem-gambling symptoms at Wave 3 (see Table 4). A one-unit increase in sensation seeking increased the odds of endorsing problem-gambling symptoms by 9%. Negative urgency at Wave 1 predicted increased severity among those who reported problem-gambling symptoms at Wave 3: A one-unit increase in negative urgency was associated with an 8% increase in the number of reported problem-gambling symptoms.
Analyses Excluding Those With BE + PG
Binge eating
When those with comorbid BE + PG were excluded from analyses, the same pattern of results emerged for women and men with one exception. In both models, elevated sensation-seeking scores were associated with reduced binge eating. Among women, a one-unit increase in sensation seeking decreased the odds of binge eating by 8% (B = .081, OR = .92, z = 2.17) and among men, a one-unit increase in sensation seeking was associated with an 18% decrease in the number of reported binge episodes (B = −.20, z = −2.284).
Problem gambling
When those with comorbid BE + PG were excluded from analyses, there were no significant predictors of future problem-gambling symptoms among women or men.
DiscussionThis study was an investigation of the degree to which three facets of impulsiveness predicted the presence and severity of future binge-eating and problem-gambling symptoms in a population-based sample of women and men. Negative urgency was a common predictor of future binge eating and problem gambling in women and men. Among men, negative urgency predicted increased severity of binge eating and problem gambling. Among women, high levels of negative urgency predicted increased odds of engaging in each behavior; however, it was not associated with increases in severity among those who engaged in the behaviors, consistent with the findings of Fischer et al. (2013). It is possible that our study did not replicate previous findings of positive associations of negative urgency with the severity of addictive behaviors and binge eating (Fischer & Smith, 2008) because we used a different statistical approach. By separating those who engaged in each behavior from those who did not, ZINB/ZIP models provided a more nuanced understanding of the association between impulsiveness and both binge eating and problem gambling. Our findings generally suggest that negative urgency places women at risk for engaging in each behavior, but if they have already begun binge eating or endorsed symptoms of problem gambling, other factors may be associated with increases in severity, perhaps such as daily fluctuations in negative affect or stress levels (Berg et al., 2013).
As expected, negative urgency was the strongest risk factor for binge eating and problem gambling among women; however, unexpectedly, it was also the strongest risk factor for each behavior among men. Our findings are consistent with cross-sectional studies of women and men that have shown that high levels of negative urgency are associated with many types of psychopathology, including binge eating and purging, alcohol and substance abuse, problem gambling, nonsuicidal self-injury, and symptoms of personality disorder (Fischer et al., 2008; MacLaren et al., 2011; Mullins-Sweatt, Lengel, & Grant, 2013; Peters, Upton, & Baer, 2013; Ruiz, Pincus, & Schinka, 2008; Widiger & Costa, 1994).
We did not find any disorder-specific personality traits associated with binge eating or problem gambling among women. However, our results showed that age discriminated between women who engaged in binge eating versus problem gambling. Women who were younger were more likely to report binge eating, whereas women who were older reported more problem-gambling symptoms. These age-related findings are consistent with existing research that suggests that binge eating begins at an earlier age (e.g., mid-20s) compared with problem gambling (e.g., middle age; American Psychiatric Association, 2013).
We found evidence of disorder-specific personality predictors of each behavior among men only. Lack of persistence was a significant predictor of binge eating, consistent with our hypothesis that lack of persistence would be a more prominent predictor for men. This pattern of association is partially consistent with results from a longitudinal study conducted by Peterson and Fischer (2012) in which lack of persistence significantly predicted future binge eating; however, our results also differ from those of Peterson and Fischer (2012) because we did not find a significant relationship between lack of persistence and binge eating among women. In addition, high levels of sensation seeking predicted increased odds of endorsing problem-gambling symptoms in men. This was an unexpected finding because the vast majority of cross-sectional research has failed to find significant associations between sensation seeking and problem gambling (e.g., Fischer & Smith, 2008; Reid et al., 2011), and the only previous longitudinal study found that sensation seeking was not a significant predictor of increases in gambling behavior over time (Cyders & Smith, 2008). However, the differences were numerous between our study and that of Cyders and Smith (2008), which may help explain the discrepant findings, including differences in sample composition (i.e., undergraduate vs. community), sample age ranges (i.e., young adults vs. adults 18–65 years old), different outcome variables (gambling engagement vs. gambling problems), and different follow-up periods (i.e., 8 months vs. 3 years). Perhaps most important, our research questions differed from Cyders and Smith, in that in our study, we examined whether sensation seeking predicted future problem-gambling symptoms, and Cyders and Smith investigated whether sensation seeking predicted changes in gambling behaviors over time. Our sample is arguably more generalizable than those of previous studies because it included a large sample of men from various age groups in the community and therefore, our results may provide a more accurate picture of the relationship between sensation seeking and problem gambling among adult men.
Another unexpected finding was that elevated sensation seeking scores were associated with decreased risk of binge eating among women and men who did not endorse problem-gambling symptoms. The vast majority of existing research has found nonsignificant associations between sensation seeking and binge eating (e.g., Peterson & Fischer, 2012) and a meta-analysis conducted by Fischer et al. (2008) found a small but significant positive association between sensation seeking and symptoms of bulimia nervosa. Our results may differ from those of previous studies because our study included women and men from various age ranges, we examined the symptom of binge eating outside the context of bulimia nervosa, other studies did not exclude individuals with comorbid problem-gambling symptoms, and/or because we used a different statistical technique. More research is needed to examine whether sensation seeking is a risk factor or a protective factor for binge eating, as this distinction is important for informing treatment and prevention efforts.
The findings from this study have important implications for the design of gender-specific treatments. They suggest that the specific impulsiveness traits that are targeted in treatment may need to vary depending upon the gender of the client. It is important to note that, because binge eating and problem gambling were both associated with the same personality facet (i.e., negative urgency), our results suggest a need to treat the underlying vulnerability associated with a particular behavior rather than simply focusing on the behavior itself. For example, a treatment program that just focused on reducing binge eating without addressing negative urgency might result in the individual transferring his or her impulsive tendencies onto a new behavior (i.e., gambling) when they feel strong negative emotions. In fact, there is evidence of addiction transfer in women who have received bariatric surgery and men in recovery from substance addiction. Two studies found increased substance-use problems among women who had received bariatric surgery, the majority of whom (68% to 70%) did not have any problems with substance use prior to the surgery (Fogger & McGuinness, 2012; Reslan, Saules, Greenwald, & Schuh, 2014). In another study, recovering men with substance addictions reported using food as a substitute for drugs, as well as increased binge eating, in early recovery (Cowan & Devine, 2008). Thus individuals who receive treatment for problems related to eating and substance may be at increased risk of developing additional problematic behaviors in recovery.
A key strength of this study is that the sample included large numbers of women and men, which allowed us to systematically investigate gender differences in risk factors for binge eating and problem gambling. In addition, using a population-based sample with a wide range of ages means the results are generalizable to adult community dwellers, particularly due to our use of sampling weights. Nevertheless, we note five limitations to this study. First, although research indicates that there are numerous facets of impulsiveness that can be assessed using either self-report measures or behavioral tasks (Cyders et al., 2007; Voon et al., 2014), the current study only examined three of these facets using self-report measures. Future studies should evaluate facets of impulsiveness not studied here such as lack of planning and positive urgency (i.e., tendency to engage in rash action when experiencing strong positive emotions) and incorporate behavioral measures of impulsiveness to gain a more comprehensive understanding of the role that impulsiveness plays in different types of disorders. Second, our sample was composed of primarily older, White, well-educated individuals, so our results should be extrapolated with caution to younger, non-White, less-educated individuals. Third, as research has shown that respondents often report higher levels of binge eating on the EDE-Q (Fairburn, 2008) compared with the EDE interview (Celio, Wilfley, Crow, Mitchell, & Walsh, 2004) because of broader definitions of binge eating by community members versus eating-disorder experts, our results should be interpreted with caution (Celio et al., 2004). Fourth, we did not assess other psychiatric disorders and so we were unable to determine the extent to which other comorbidities could have had an influence on binge eating or problem gambling. Finally, binge eating was assessed over a 28-day time frame, whereas problem gambling was assessed over a 12-month time frame, which limits our ability to draw direct comparisons between these behaviors.
ConclusionBased on these findings, we can conclude that there are transdiagnostic and disorder-specific facets of impulsivity that predict future binge eating and problem gambling. Negative urgency represents a shared vulnerability for binge eating and problem gambling in women and men. We found evidence of gender differences in certain disorder-specific traits: Among men, lack of persistence predicted binge eating and sensation seeking predicted problem gambling. We found no disorder-specific personality predictors for women; however, younger age predicted binge eating and older age predicted problem gambling. Identification of disorder-specific traits may help explain why individuals may engage in one behavior over another. For example, men who have difficulty persisting on tasks may be more likely to binge eat whereas men with elevated levels of sensation seeking may be more prone to gamble.
Overall, the results of this study do not provide clear support for the conceptualization of binge eating as an addiction. Although we found evidence of shared personality traits between binge eating and problem gambling, these traits are related to many forms of psychopathology and are not specific to addiction (Ruiz et al., 2008; Widiger & Costa, 1994). In addition, we found evidence of personality facets specifically related to problem gambling (sensation seeking) and binge eating (lack of persistence) among men, which demonstrates that the two behaviors have distinct personality correlates. Sensation seeking is a personality characteristic that has also been associated with alcohol abuse (Curcio & George, 2011; Cyders, Flory, Rainer, & Smith, 2009) and therefore may represent a trait that is specifically associated with other addictions. The relationship between personality and psychopathology is complex: Personality traits could serve as risk factors for, or consequences of, particular disorders, or both personality traits and the given disorder could be caused by a third variable, such as poor self-regulation (Lilenfeld, Wonderlich, Riso, Crosby, & Mitchell, 2006). Thus future researchers need to investigate similarities and differences between binge eating and other substance and behavioral addictions to more fully evaluate the validity of conceptualizing binge eating as an addiction.
Footnotes 1 According to Cohen (1992), d = 0.2 corresponds to a small effect size, d = 0.5 corresponds to a medium effect size, and d = 0.8 corresponds to a large effect size.
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Submitted: September 3, 2014 Revised: January 23, 2015 Accepted: February 3, 2015
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Record: 66- The influence of self-exempting beliefs and social networks on daily smoking: A mediation relationship explored. Yang, Xiaozhao Y.; Kelly, Brian C.; Yang, Tingzhong; Psychology of Addictive Behaviors, Vol 28(3), Sep, 2014 pp. 921-927. Publisher: American Psychological Association; [Journal Article] Abstract: The decision to initiate, maintain, or quit cigarette smoking is structured by both social networks and health beliefs. Self-exempting beliefs affect people’s decisions in favor of a behavior even when they recognize the harm associated with it. This study incorporated the literatures on social networks and self-exempting beliefs to study the problem of daily smoking by exploring their mediatory relationships and the mechanisms of how smoking behavior is developed and maintained. Specifically, this article hypothesizes that social networks affect daily smoking directly as well as indirectly by facilitating the formation of self-exempting beliefs. The sample comes from urban male residents in Hangzhou, China randomly selected and interviewed through multistage sampling in 2011. Using binary mediation analysis with logistic regression to test the hypotheses, the authors found that (a) daily smoking is associated with having smokers in several social network arenas and (b) self-exempting beliefs about smoking mediate the association between coworker network and daily smoking, but not for family network and friend network. The role of social network at work place in the creation and maintenance of self-exempting beliefs should be considered by policymakers, prevention experts, and interventionists. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Influence of Self-Exempting Beliefs and Social Networks on Daily Smoking: A Mediation Relationship Explored / BRIEF REPORT
By: Xiaozhao Y. Yang
Department of Sociology, Purdue University
Brian C. Kelly
Department of Sociology, Purdue University
Tingzhong Yang
Research Center for Tobacco Control, Zhejiang University;
Acknowledgement:
Smoking, like many other health behaviors, is subject to the complex interaction of social influences and psychological processes. Intervention through policy is a necessary action but a difficult to carry out, especially when professional conceptions confront lay understandings. People tend to be reluctant to act upon mass communication messages unless the information is transmitted via their own social networks (Katz & Lazarsfeld, 1955; Bandura, 2004). Sometimes implemented policy even creates contrary effects due to a marginalizing impact, thus making certain groups harder to reach and more policy-resistant when reached (Graham et al., 2006; Room, 2005; Stuber, Galea & Link, 2008; Constance & Peretti-Watel, 2010). Given the contextualized nature of policy uptake, policy implementation that targets tobacco users must be attentive to the social network composition of smokers and its effect on their health beliefs. This study identifies how social networks influence smoking through a particular set of health beliefs: self-exempting beliefs (SEB).
SEB and SmokingAlthough the physical health effects of consuming tobacco has a medical basis, the belief in such basis is a sociopsychological construct. Festinger (1957) proposed the concept of SEB: When people receive messages about the risk of their behavior, many of which refer to scientific evidence, instead of surrendering to the message and changing their behavior, they resort to beliefs that exempt themselves to mitigate the undesirability of such behavior. People may actively use their own evidence contrary to medical professional recommendations or acknowledge professional suggestions but argue for the exception for themselves. For example, smokers may develop explanations for why medical evidences do not apply to them (Heikkinen et al., 2010; Oakes, Chapman, Borland, Balmford, & Trotter, 2004). SEB reduce the threat to self-integrity and its resulting behavioral adaption; they may rise à posteriori to justify the existing behavior or strengthen the behavioral continuance to weaken the necessity of quitting (Radtke, Scholz, Keller, & Hornung, 2011); they could also spread as innovative messages that ease the anxiety about tobacco harm and recruit others into cigarette smoking. SEB also emerge when smoking can be used as a useful label of identity and provide a form of social etiquette for instrumental benefits (Collins, Maguire, & O’Dell, 2002). Some people who possess SEB try to distance themselves from the addiction stereotype but nevertheless continue to use cigarettes because of their supposed normative status and predominance in social interaction. In this aspect, men in Chinese society routinely use the phenomenon of courtesy smoking and gifting cigarettes, where cigarettes are used in daily interactions among men, welcoming guests, or as bribe (Ma et al., 2008; Rich & Xiao, 2012). Sociologists (Swidler, 1986; DiMaggio, 2001; Vuolo, 2012) have argued that culture constitutes an elastic reservoir for individuals to choose according to their needs when values are in conflict. Thus, culture also plays an important role in creating concrete SEB. For example, Wright illustrated with ample descriptions of how farmers in Kentucky justify tobacco cultivation and consumption as a community tradition and cultural necessity (Wright, 2005). Other scholars (e.g., Manderson, 1981; Jackson et al., 2004) discovered that beliefs in nonharmful ways of tobacco smoking, based on the semireligious folk classification of tobacco as food, indirectly legitimizes smoking as normal daily conduct. Scholars have reported SEB about smoking are associated with education, age, and other background factors (Chapman et al., 1993; Oakes et al., 2004; Heikkinen et al., 2010).
Social Networks and SmokingSocial networks matter because different types of connections have different influences on behaviors. Social networks provide contexts where communications, consolidations of beliefs, and daily interactions are performed. Sutherland and Cressey (1970) argued that typical delinquent behaviors are gradually formed by both peer pressure and the changed attitude from imitating and learning from peers. When people cluster within networks with smokers, the observation of smoking behavior is repeated and normalized to a degree that not only will the perceived normative status of smoking be confirmed in their cognition, but justifications of such behavior could also be mutually reinforced by people who smoke within the social network. Scholars have argued the strength of ties in a network has the unique function to influence beliefs and behavior, where weak ties facilitate heterogeneous information and behavior, strong ties generate conformity to existing norms (Granovetter, 1973; Baer, 2010). Thus the impact on smoking by networks composed of different types of ties, weak versus strong, family versus friends, distant versus proximate, can too be different.
A number of studies have also discussed the impact of different types of social network ties on smoking. Family influence is strongest at earlier stages of the life course but is replaced by peer influences as the individual grows older (Glynn, 1981; Krosnick & Judd, 1982; Perry, Kelder, & Komro, 1993); although some studies have found that parental influence does not entirely diminish after years (Chassin et al., 1986; de Vries et al., 2003). Another study by Christakis and Fowler (2008), with specific attention to smoking networks, revealed that cessation is most likely to occur when one’s spouse stopped smoking. In this manner, both natal families and marital families matter. Moreover, a network’s impact on an individual’s smoking probability also differs across social settings: such as the work unit, family, or a cultural setting. For example, family members’ attitudes and intervention constitute the strongest predictor of Chinese men’s smoking cessation (Yang et al., 2006; Zhang et al., 2012).
SEB are arguably developed not only as a defensive mechanism but also as part of social routines within social networks. Currently, there is no study that has investigated SEB as contextualized in social networks, though it has been noted that cognitive dissonance, risk perception, and behavior motivation are joint products of social and psychological mechanisms (Bandura, 2004). The perception of health risk is organized and transmitted by network interactions, built upon the consistently perceived attitude and (mis)information grouped in cliques (Scherer & Cho, 2003; Helleringer & Kohler, 2005; Kohler, Behrman & Watkins, 2007). Thus, individuals can develop SEB, particularly the culturally specific content of such beliefs, from family members’ gradual socialization throughout years, daily communication with friends, integration into a subculture, or influence by those who work closely with him. Some have demonstrated that smokers rarely exert direct pressure on their nonsmoking peers, and the latter initiated smoking rather because the discouraging message is scarcely received (Urberg et al., 1990). Others suggest (Kelly, 2009; Constance & Peretti-Watel, 2010) that friends may express justifications for their smoking peers vis-à-vis coercive policies. Therefore, holding certain SEB does not necessarily require the status of being a smoker. Instead, being tied to smoking networks alone can locate a nonsmoker in the midst of messages and information shared by smokers and increase his likelihood of having SEB. Thus, SEB are more likely to occur in contexts where one’s network ties include smokers. As a result, how social networks affect smoking behavior would be mediated by SEB
Based upon the literature reviewed above, we propose the following hypotheses:
H1: Men with higher levels of SEB are more likely to be daily smokers.
H2a: Men reporting smokers in their family network are more likely to be daily smokers.
H2b: Men reporting smokers in their friend network are more likely to be daily smokers.
H2c: Men reporting smokers in their coworker networks are more likely to be daily smokers.
H3: SEB will mediate the association between three social network types and smoking status.
Methodology Sampling
A multistage sampling design was employed to collect data during the summer of 2011. At Stage 1, we randomly selected two residential districts (qu) of Hangzhou; at Stage 2, we randomly selected two subdistricts (jiedao) from a district; then two to three communities (shequ) within each subdistrict at Stage 3. Hangzhou is located in southeast China with a population of 6.7 million, it has six districts and 16–22 communities within each district. The Community Committee Office randomly sampled households in each community, and these households were distributed across each community in approximate proportion to their estimated overall distribution across the city cluster of communities. Participants in the study were sampled independently within these clusters. The inclusion criterion was being a resident aged 15 years or older. One eligible resident from each household was selected into the study, based on nearest birthdate to the interviewing date. We scheduled a face-to-face individual survey once an eligible individual was identified and agreed to study participation. All surveys were conducted by means of a structured, interviewer-administered questionnaire. Surveyors were second-year medical graduate students or fourth-year medical students. Each surveyor completed a training on the study protocol and survey procedures prior to working on the study. Questionnaires were administered privately to participants in their home or in a quiet place, such as a backyard or community park. Appointments were scheduled through a community organization and were rescheduled as necessary. Upon receiving instructions from surveyors, participants were asked to fill out a questionnaire of approximately 30 minutes’ duration. Each participant was afforded an opportunity to seek clarification of questions regarding the survey or questionnaire items, and given adequate time for completion. The protocol was approved by the Ethics Committee at the Medical Center, Zhejiang University, and we obtained informed written consent from all participants prior to interview. The total sample size yielded was 669. The sample’s demographic characteristics are shown in Table 1.
Descriptive Demographics
Measurement
SEB
SEB are measured by 20 items. Each item asked respondents to rate their agreement on the statement in a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Eighteen items are adopted from Oakes’s study (Oakes et al., 2004), such as “The scientific evidence about tobacco’s harm is exaggerated,” “Many smokers are very healthy so smoking can’t be so harmful,” “My current smoking amount is too low to be considered risky,” “You have got to die of something, so why not enjoy yourself and smoke,” “Smoking is no more risky than lots of other things that people do,” and so forth. Two additional items to represent the instrumental dimension of SEB were included after pilot tests within local exploratory studies (Ma et al., 2008; Yang et al., 2006), “smoking is good for socializing” and “smoking is good for reducing stress.”
Network measures
Smoking social networks are measured by three questions, representing three types of ties. The strongest ties in Chinese culture usually refer to the immediate family, so the questionnaire first asks if there is anyone who smokes in respondent’s household. Please note, word household (jia) in the Chinese language has the same connotation with family (jia). There is another word (qinqi) for extended family who usually live somewhere else. This term jia is indicative of the family with whom the individual shares a household. In this instance, family household may include one’s nuclear family, parents, or possibly siblings. The second question asks if the respondent’s friends smoke. The third question intends to represent formal weak ties to smoking associates: Does any coworker who shares an office or working space with the respondent smoke.
Smoking status measures
Smoking status of the respondent is first assessed by a question that asks respondent’s current smoking status: 1, smoke everyday; 2, smoke some days; 3, don’t smoke now. Considering the high prevalence of Chinese male smoking and the normative status of cigarettes in social etiquette, it is often futile to distinguish occasional smokers versus nonsmokers, as well as smoking under pressure versus absolute abstinence among men, therefore it is most important to distinguish daily habitual smokers from nondaily smokers. Furthermore, Brant test of parallel regression assumption dictates the ordinal treatment of the dependent variable inappropriate (Long & Freese, 2006). Therefore people who answered they smoke everyday are coded as a daily smoker (1), and the rest of them are grouped as not daily smokers (0).
Demographic control measures
Demographic indicators were assessed in the section “individual and family background” of the survey, including sex, age, income, marriage status, ethnicity, and education. Religious belief is simply dichotomized as “Do you have any religious beliefs or not,” because of the sensitive nature of religion and lack of major religious beliefs in the survey context.
Analytic Method
To confirm the reliability of the SEB measure, a Cronbach’s alpha test was performed on the 20 SEB items. Cronbach’s alpha over 0.70 suggests acceptable level of reliability; that which over 0.80 suggests considerably good reliability. Cronbach’s alpha test of our SEB measures (0.92) suggests excellent reliability.
To conduct mediation analyses, we combined the classic mediation model proposed by Baron and Kenny (1986)—which emphasized the reduction of coefficients and p value for the independent variable after mediator is introduced—with survey sample weighted bootstrap to estimate the mediation effects and coefficients. Recently scholars argued that observing the change of power by the classic approach does not suffice to establish mediation for two main reasons: First, it is possible to have significance reduction without considerable effect size change, or considerable effect size change without reducing significance; second, under Baron and Kenny’s classic approach, people often erroneously treat the mediator as a control variable, or vice versa (Zhao et al., 2010). As a result, Preacher and Hayes (2008) proved that a significance test on the indirect effect can avoid Type I error and is more straightforward than the classic approach. Therefore, we follow the recent technique of using bootstrap to test the significance of indirect effect for the mediation analyses (Bollen & Stine, 1990; Shrout & Bolger, 2002; MacKinnon et al., 2002).
Another benefit of using bootstrap resampling is its capacity to deal with multistage cluster sampling. The svy bootstrap command in Stata also allows us to deal with complex data structure set by sample weighting, plus using bootstrap to resample observations. In this study, district, neighborhood, and community, are the clusters specified for bootstrap resampling. Sample weight is calculated as the inverse product of the probabilities that each level’s unit is selected from its population: within n districts, l communities are in jth neighborhood, and m neighborhoods from ith district, weight Wij equals
, where the population total at each level is attained from various bureau websites. Binary mediation logistic regression was performed in STATA 11 for the analyses, then we use survey bootstrap to derive the final coefficients and 95% confidence intervals. Adjusted odds ratios, significance level at 95%, indirect effect, and ratio of indirect to direct effect are reported.
Results Sample Characteristics
The percentage of cases, sample mean, standard deviation, and range are presented for both main variables and relevant demographic characteristics in Table 1. The outcome variable, smoking status, shows an approximate Pareto number: 39.5 of all males in the sample are self-described as smoking on a daily basis, whereas 60.5% of them are not daily smokers. The ratio of daily smoking generally conforms with recent studies in the same city (T. Yang et al., 2007; Yang et al., 2010). Missing data is negligible among all the variables as only three cases were missing from the SEB instrument.
Smoking is common in social networks. In this sample, 53.9% of male respondents reported having a smoker in their families, 91.7% reported having a smoker among their friends, and 71.8% indicated having a smoker among coworkers. The mean of SEB among men is 2.09, standard deviation is 0.78, and the SEB scale ranges from 1 to 5.
The socioeconomic background of the sampled male population is diverse although all respondents had the same background as having been a resident in Hangzhou for at least 1 year. The largest proportion are those who claimed yearly household income per capita between 20,000 and 30,000 Yuan (25.6%), about a quarter of all males belong to this income category. In terms of education, 10.6% had received only elementary education or below, 29.5% went to middle school, 24.6% went to high school, and 35.4% received a college degree. Among sampled males, 24.6% are unmarried, 72.4% are married, and the remainder (3.1%) are divorced or widowed. The mean age of the sample is 42 (SD = 16), respondent ages ranged from 15 to 87 years old. As for religion, 13.7% reported having religious beliefs, 86.3% reported no religion.
Findings
Table 2 shows that higher SEB is associated with higher odds of being a daily smoker. This relationship is considerably strong and straightforward after controlling for other demographic influences including income, age, marriage, religious beliefs, and education. The odds ratio of being a daily smoker for SEB is 3.52 (p < .001), which implies that with each higher level of SEB, the odds of being daily smoker is 3.52 times higher. Additionally worth noting is the impact of income, age, and education: Income increases the likelihood of being daily smoker while older age and education reduces it.
Logistic Regression Analyses of Daily Smoker Status on Self-Exempting Beliefs (SEB) and Social Networks Independently
The association of each type of social networks and smoking status is analyzed independent from SEB while controlling for the same demographic factors and shown in Table 2. As expected, the presence of smokers in all three network types independently increase the odds of daily smoking. Having a smoker in one’s coworker network has the largest association with the likelihood of being daily smoker in terms of odds ratio magnitude (OR = 4.74, p < .001), followed by the friend network (OR = 3.73, p < .05) and family network (OR = 2.65, p < .001). The second tier of hypotheses (H2a, H2b, and H2c) is hence validated by the statistics presented in the first model of Table 3: Men are more likely to be daily smokers when having a smoker in his family, friend, or coworker network, net of the effect of the control variables.
Unmediated models, Self-Exempting Beliefs (SEB)-Mediated Models, by Types of Social Networks
The upper part of Table 3 shows the unmediated logistic regression of smoking status on social networks, each network is estimated controlling for the other types of networks and demographic variables. The unmediated models in Table 3 are exactly the same, they are listed separately for the sake of convenience to compare with the mediated models. The unmediated model shows that family network and coworker network are significantly associated with being daily smoker after controlling for the other network types and demographic variables. Having smoking family member increases the probability of being a daily smoker by 2.10 (p < .01), and having smoking coworker increases the probability of being a daily smoker by 3.94 (p < .001). Friend network is not significantly associated with being daily smoker after controlling for the other network types, a difference from Table 2 where only demographic variables are controlled.
The lower part of Table 3 shows the mediation analyses where social networks are mediated by SEB, controlling for the other network types and demographic variables. The classic approach expects reduction in p value or the magnitude of odds ratio of social network when SEB was added to the regression, and the survey weighted bootstrap method calculates the odds ratio and indirect effects. The magnitude of odds ratio of smoking status on family network actually increased after SEB was introduced as a mediator (2.23 vs. 2.10), and the 95% confidence interval of the indirect effect overlaps zero. The ratio of indirect effect to direct effect for the family network model is also minimal (0.01). We conclude there is no mediation effect in the family network model. For the friend network model, indirect effect is too not significant. The ratio of indirect effect to direct effect is large in absolute value (2.02), but indeed originates from an inconsistent mediation in this case (MacKinnon, Fairchild, & Fritz, 2007). Because friend network itself is not significantly associated with daily smoking, we conclude there is no mediation effect for the friend network model. However, for the coworker network, odds ratio drops from 3.94 to 3.53 after SEB was introduced. The indirect effect has an above zero 95% confidence interval (0.03–0.10), the indirect/direct effect ratio is 0.23. The result suggests that SEB partially mediates coworker network and smoking status, after controlling for the other network types and demographic variables.
DiscussionThis study was designed to examine how SEB are related to daily smoking, and whether the relationship between social network types and smoking are mediated by SEB. Previous studies had theoretically established the relationship between SEB and smoking initiation, continuation, and cessation. Relatively few investigations about SEB and smoking have been conducted in developing countries, and we are curious about how the normative status of cigarettes in such societies may shape the understanding of smoking behavior among smokers and nonsmokers alike. Our study attends to these issues as well.
This study, which adopted an existing SEB scale and incorporated new items for the instrumental dimension of SEB, has demonstrated a strong relationship between being a daily smoker and having higher levels of SEB. In contexts such as China, where cigarette use is normative but antitobacco policy and prevention efforts are also taking place at the same time, lay understandings and views of smoking are very ambiguous. Historically, tobacco was prescribed to people as medicament because of its assumed function to balance the corporeal air system (qi) in the philosophy of Chinese traditional medicine (Du, 2000). Dikötter, Laamann, & Xun (2002) also argued that, besides its assumed medical benefit, the quick and vast acceptance of tobacco in China was closely associated with the phenomenological meaning of smoke (air or qi) found in Chinese folk religion’s evil dispelling ceremony. This mythical form of belief persists, and culminated during the 2003 SARS outbreak when people circulated the message that smoking cigarettes and burning incense can prevent SARS (Tai & Sun, 2011). Ma’s exploratory study (Ma et al., 2008) informs us that the most common SEB about smoking among Chinese males include the importance of cigarettes in social and cultural etiquette. Although concrete beliefs in the legitimacy of tobacco consumption may be varying, they all demonstrated that traditional culture in many cases could provide the fundamental basis of SEB among men in China. People respond to tobacco policy positively overall (X. Y. Yang, Anderson, & Yang, 2014), but they also condone smoking as a necessary part of social etiquette, gender identity, or existing tradition. Tobacco policymakers need to be aware of the existence of SEB and consider counteractive measures.
The study results showed that smoking in each network included in the hypotheses exhibited strong positive association with being a daily smoker when estimated independent of the other types of networks. It is beyond the scope of this article to rule out the auto-selection effect of smoking behavior, but it is possible to reason that although friend networks can be formulated initially by individual selection of the homophily principle, that is, “birds of the same feather, fly together.” Individuals have much less control over which working unit or family they are embedded in. Apart from the direct effect of social networks on smoking behavior, the research results discovered the significant indirect effect of SEB as a mediator between coworker networks and smoking behavior. But SEB’s mediation is not significant for family networks and friend networks, therefore it can be inferred that a coworker network, as a deposit of weak ties, can influence the individual to smoke through the development of SEB, which partially confirmed hypothesis H3. One can argue that because our respondents are urban male residents in China, their referred smokers in family networks are likely offspring or parents, but rarely siblings or spouses due to one child policy and the fact that few women smoke in China. Network alters such as parents and offspring fill in the family roles that are less conducive to spread beliefs due to role expectations and the generation gap. On the other hand, the definition of a friend may require further refinement to distinguish it as an effective carrier of SEB. Coworker corresponds to a type of weak tie (low in emotional intense and interaction frequency), which scholars argued is prone to transmit information. (Granovetter, 1973)
Although there are many strengths, this current study has a few limitations. First, the friend network measured in this study could be defined more specifically as different types and the count of friends rather than being categorized under the broad term friend. Thus we may discern more fundamental differences between the smoking patterns of a coworker network and friend network. Second, although we have argued that family network and coworker network are not likely caused by smoking as a result of homophily, we cannot confirm the causal direction between SEB and smoking solely from the cross-sectional data used here. Longitudinal data would be able to answer some interesting questions suggested by this study, such as how beliefs about smoking changed with formation and dissolution of social networks, and how people’s smoking status and behavior change with SEB over a period of time. Finally, this study only included male population for analysis because the great difference of smoking prevalence between Chinese men and women could bias the estimation in the model we employed. However, future studies could Yang incorporate both males and females using a different model. Despite these limitations, this article provides important information on the role of social networks in SEB that play a pivotal role in the justification of smoking behaviors.
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Submitted: November 14, 2013 Revised: March 31, 2014 Accepted: April 30, 2014
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Record: 67- The relation between stress and alcohol use among Hispanic adolescents. Goldbach, Jeremy T.; Berger Cardoso, Jodi; Cervantes, Richard C.; Duan, Lei; Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015 pp. 960-968. Publisher: American Psychological Association; [Journal Article] Abstract: We explored the relation between 8 domains of Hispanic stress and alcohol use and frequency of use in a sample of Hispanic adolescents between 11 and 19 years old (N = 901). Independent t tests were used to compare means of domains of Hispanic stress between adolescents who reported alcohol use and those who reported no use. In addition, multinomial logistic regression was used to examine whether domains of Hispanic stress were related to alcohol use and whether the relation differed by gender and age. Multiple imputation was used to address missing data. In the analytic sample, 75.8% (n = 683) reported no use and 24.2% (n = 218) reported alcohol use during the previous 30 days. Higher mean Hispanic stress scores were observed among youths who reported alcohol use during the previous 30 days in 5 domains: acculturation gap, community and gang violence, family economic, discrimination, and family and drug-related stress. Increased community and gang violence, family and drug, and acculturative gap stress were found to be associated with some alcohol use categories beyond the effect of other domains. Few differences in the association between Hispanic stress and alcohol use by gender and age were observed. Study findings indicate that family and drug-related, community and gang violence, and acculturative gap stress domains are salient factors related to alcohol use among Hispanic adolescents, and their implications for prevention science are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Relation Between Stress and Alcohol Use Among Hispanic Adolescents
By: Jeremy T. Goldbach
School of Social Work, University of Southern California;
Jodi Berger Cardoso
Graduate College of Social Work, University of Houston
Richard C. Cervantes
Behavioral Assessment, Incorporated, Beverly Hills, California
Lei Duan
School of Social Work, University of Southern California
Acknowledgement: Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number 2R44MH073180-02 to Richard C. Cervantes.
More than one quarter of Hispanic adolescents in the United States report using alcohol during the previous 30 days (Pemberton, Colliver, Robbins, & Gfroerer, 2008; Substance Abuse and Mental Health Services Administration [SAMHSA], 2010). Compared with their non-Hispanic White counterparts, Hispanic adolescents report higher rates of binge and heavy drinking (Kanny, Liu, Brewer, & Lu, 2013; King & Vidourek, 2010). The health consequences of alcohol consumption, especially during adolescence, are numerous. Adolescents who engage in drinking have a higher prevalence of involvement in criminal activity, abuse of other substances, and depression (Hill et al., 2010) and report poorer academic outcomes and more frequent risky sexual behavior (Miller, Naimi, Brewer, & Jones, 2007). Adolescent alcohol use has been linked to poor cognitive functioning, lower mood states (Townshend & Duka, 2005), and poor decision-making, particularly among individuals who begin drinking at an early age (Goudriaan, Grekin, & Sher, 2007). Understanding the underlying cultural mechanisms that may contribute to alcohol use or promote abstinence patterns in this population is a public health priority (Little, 2000; Longabaugh, 2007).
Stress and Substance Use Among Hispanic AdolescentsResearchers commonly suggest that a stress-illness model (Lazarus & Folkman, 1978) explains behavioral health disparities among minority groups (Edwards & Romero, 2008; Williams & Mohammed, 2009). Among Hispanics, stressors include those associated with cultural adaptation (e.g., acculturation) and minority status (e.g., discrimination, immigration; Córdova & Cervantes, 2010). More than two decades of research with Hispanic adults has found a relatively consistent association between these unique Hispanic stressors and alcohol use (Amaro, Whitaker, Coffman, & Heeren, 1990; Caetano & Clark, 2003; Okamoto, Ritt-Olson, Soto, Baezconde-Garbanati, & Unger, 2009; Vega, Sribney, & Achara-Abrahams, 2003).
Few studies have examined these stressors and alcohol use among Hispanic adolescents (Delva et al., 2005) and have reported inconsistent findings. Some researchers found no association between acculturation and alcohol use (Elder et al., 2000), whereas others have reported positive (Brindis, Wolfe, McCarter, Ball, & Starbuck-Morales, 1995; Caetano, Ramisetty-Mikler, Caetano Vaeth, & Harris, 2007; Polednak, 1997) or negative (Nielsen & Ford, 2001) associations. This may be due to the complex nature of stress among Hispanic youths. For example, Gil, Wagner, and Vega (2000) found that acculturative stress is related to alcohol use among adolescent Hispanic boys, but is affected by additional factors such as the deterioration of Hispanic family values, attitudes, and behaviors. Research including measures of family stress (Driscoll, Biggs, Brindis, & Yankah, 2001), parental warmth, and language spoken at home (Mogro-Wilson, 2008) has shown these factors also to be relevant to understanding alcohol use among Hispanic adolescents (Prado & Pantin, 2011). Furthermore, although many Hispanic families emigrate from rural areas (Suárez-Orozco & Suárez-Orozco, 2001), they tend to reside in more geographically concentrated and urban areas once arriving in the United States (U.S. Census Bureau, 2007). Higher-density households and living arrangements found in Hispanic-dominant urban neighborhoods with fewer socioeconomic advantages also influence family and individual stress levels (Cervantes, Córdova, Fisher, & Kilp, 2008), which may influence alcohol use patterns. It is clear that stress experiences associated with alcohol use among Hispanic adolescents are multifaceted and not well understood.
Recently, Cervantes and colleagues (Cervantes, Fisher, Córdova, & Napper, 2012; Cervantes, Goldbach, & Padilla, 2012) developed a measure to identify and assess the impact of different stress experiences among Hispanic youths. The Hispanic Stress Inventory-Adolescent Version (HSI-A) was constructed in a two-part process: (a) focus groups to identify stressful experiences and (b) a national validation study using exploratory factor analysis with 992 Hispanic adolescents in four metropolitan U.S. cities. The item analysis revealed eight unique domains of Hispanic stress: acculturation gap, culture and educational, immigration, community and gang violence, family and drug-related, family related immigration stress, discrimination, and family economic stress. Some domains were closely associated with processes related directly to being a Hispanic in the United States (e.g., immigration stress, acculturation), whereas others reflected stressful experiences common to the neighborhoods in which Hispanics live related to socioeconomic disadvantage (Evans & Kim, 2007). For example, although the domain of community and gang violence stress includes items such as being stereotyped as being in a gang, it also includes living in a dangerous neighborhood and seeing drive-by shootings. In the family and drug-related stress domain, items include having too little contact with parents and having family members who sell drugs.
Researchers have examined the HSI-A in relation to depression (Cervantes, Berger Cardoso, & Goldbach, 2015), suicidal ideation (Cervantes, Goldbach, Varela, & Santisteban, 2014), and risky substance use behaviors (Berger Cardoso, Goldbach, Cervantes, & Swank, 2015; Cervantes, Goldbach, & Santos, 2011). To date, however, no studies have used a comprehensive measure of Hispanic stress to explore alcohol use in a sample of Hispanic adolescents. The present study filled a gap in the literature by examining: (a) how the eight domains of stress, as measured by the HSI-A, differ among adolescents who used alcohol versus those who did not use alcohol during the previous 30 days; and (b) the relation between each domain of Hispanic stress and the relative risk of alcohol use during the previous 30 days after controlling for covariates and the other stress domains. Additionally, exploratory analysis examined whether age and gender moderate the relation between stress domains and alcohol use. We hypothesized that higher scores in Hispanic stress domains will be associated with alcohol use and that community and family stressors will be associated with a higher degree of use during the previous 30 days. Additionally, national trends (e.g., Centers for Disease Control & Prevention, 2014; Chassin, Pitts, & Prost, 2002) suggest older youths and boys report more binge drinking. However, to our knowledge no studies have examined whether certain stressors affect alcohol use differently by gender or as youths advance through adolescence. As such, we explored whether age and gender moderated the effects of stress domains on alcohol use among Hispanic adolescents.
Method Sample
Data for the current study were drawn from a sample of 1,119 Hispanic adolescents between 11 and 19 years old from four urban U.S. cities: Los Angeles, Miami, El Paso, and Lawrence (a suburb of Boston). Adolescents were initially recruited from middle and high school settings to participate in a study designed to validate the HSI-A. Classroom rosters were separated by grade level and SPSS software was used to randomly select classrooms in each school to participate in the study. Only schools that reported that at least 50% of their student body was Hispanic were eligible to participate in the original study. Although socioeconomic status was not collected directly from participants, at all school sites more than 50% of youths qualified for free or reduced-price lunches.
Of the 1,119 participants, 901 provided complete information on alcohol use, HSI-A variables, and other covariates including gender, age, parental nativity, and dominant language. Participants in the sample came from diverse Hispanic backgrounds: 47.0% (n = 406) Mexican, 13.3% (n = 117) Cuban, 13% (n = 115) Dominican, 9.5% (n = 84) mixed, 7.7% (n = 68) Puerto Rican, 5.1% (n = 45) Central American, 4.0% (n = 35) South American, and 1.5% (n = 18) other. Approximately 2% (n = 18) had missing data on origin. Nearly half of the sample was from Los Angeles (n = 443, 44.7%), followed by Lawrence (n = 253, 25.5%), Miami (n = 207, 20.8%), and El Paso (n = 89. 9.0%).
To address the first research question by comparing the mean difference of HSI-A domains between alcohol users and nonusers, we performed the analysis based on the 901 participants with complete information. Regarding the second and third research questions, analyses were conducted with the sample with complete data (n = 901) and using multiple (20) imputed datasets with the full sample (N = 1,119). We describe a series of sensitivity analyses in the data analysis section designed to address potential biases related to missing data.
Measures
Survey instruments were administered to youths in their preferred language (English or Spanish) using paper-and-pencil booklets. The primary independent variable of interest was the HSI-A; the dependent variable was alcohol use, and if present, the extent of alcohol use disclosed by each adolescent.
Hispanic stress
The construct of Hispanic stress was measured using the HSI-A, a 71-item measure that assesses exposure to and appraisal of life stressors related to minority status. The HSI-A is a validated measure of stress among Hispanic adolescents and has strong overall internal consistency reliability for appraisal ratings (α = .92; see Cervantes, Fisher, et al., 2012 for more information about scale psychometrics). Previous factor analytic research has identified eight unique domains (subscales) of Hispanic stress: family economic (12 items), culture and educational (14 items), acculturation gap (12 items), immigration (seven items), discrimination (six items), family immigration (seven items), community and gang violence-related (eight items), and family and drug-related (five items) stress (see Cervantes, Fisher, et al., 2012).
Some of the stress domains capture concepts exclusively related to being Hispanic. For example, acculturative gap (“Parents want me to maintain customs and traditions,” “Expected to be like parent to siblings”), culture and educational (“Teachers think I am cheating when I am speaking in Spanish,” “School ignored cultural history”), discrimination (“Students said racist things,” “Pointed at and called me names”), immigration (“Left close friends in home country,” “Separated from some family members”), and family immigration (“Family afraid of getting caught by immigration officials,” “Family had problems with immigration papers”) relate specifically to Hispanic youths. The remaining domains, family economic (“Parents could not get a good job,” “Not enough money for everyone in the family”), community and gang violence (“I have a lot of pressure to be involved in gangs,” “Saw weapons at school”), and family and drug stress (“Family members had a drug problem,” “Hard to speak with family”), capture social stressors that are often experienced by Hispanics and other minority groups in the United States. Participants were asked whether they had experienced a specific stressor, and if so, to appraise the degree to which the stressor affected them. Responses were based on a 5-point Likert scale: 1 = not at all worried or tense, 2 = a little worried or tense, 3 = moderately worried or tense, 4 = very worried or tense, 5 = extremely worried or tense. Higher scores on the stress subscales indicate more stressful experiences. Mean values for the eight domains of Hispanic stress were centered for multivariate analysis.
Alcohol use and frequency of alcohol use
Two single-item questions from the Government Performance and Results Act’s participant outcome measures (SAMHSA, 2003) were used to assess alcohol use and frequency of alcohol use. The act features a set of required federal reporting guidelines used to collect performance outcome data on all substance abuse prevention and treatment programs funded through SAMHSA. Relying on these measurement standards, alcohol use was assessed by adolescent self-report to a single question: “In the past 30 days, did you use alcohol?” A dichotomous indicator of alcohol use was created (0 = no use, 1 = use). Similarly, frequency of alcohol use was assessed using a follow-up question: “In the past 30 days, how many times did you use alcohol?” To capture differences in alcohol risk behavior, a three-level ordinal variable was constructed to indicate no alcohol use (0 times), low use (1–3 times) and heavy use (4 or more times) during the previous 30 days (Cervantes et al., 2015). The cutoff points were chosen based on an assumption that youths who drink fewer than 4 times per month (i.e., 1 to 3 times) are likely drinking on average less than once per week. We suspect that adolescents reporting drinking 4 or more times per month are more likely drinking with consistency (that is, about once per week). We considered including alcohol frequency as a continuous variable, yet the frequency of alcohol use was skewed to the lower response categories. In line with previous work by Berger Cardoso, Goldbach, and Cervantes (in press), we used the aforementioned cutoffs. Although these cutoffs provide some insight into drinking patterns, a validated measure of binge or heavy drinking would have likely provided a more accurate assessment of use in the sample.
Covariates
We controlled for four demographic variables related to alcohol use and frequency among adolescents (Wade, Lariscy, & Hummer, 2013; Warner et al., 2006; Weiss & Tillman, 2009). Adolescent age was included as a continuous variable and centered for the analysis testing the moderation effects of age on alcohol use. Gender was a dichotomous variable (male or female) and was included as both a control and interaction variable. Other categorical variables included parental nativity (0 = both parents born in the United States, 1 = one or both parents born outside the United States) and dominant language (1 = English, 2 = Spanish, 3 = other). Two dummy variables for dominant language were created (the reference was other) and controlled for in the analyses to minimize confounding factors when examining the relation between Hispanic stress and alcohol use and frequency of alcohol use in this population.
Data Analysis
We examined descriptive differences in age, gender, parental nativity, dominant language, and Hispanic stress domains between adolescents who reported alcohol use and those who did not report use during the previous 30 days. In addition, effect sizes (Cohen’s d) for the standardized mean differences in the eight stress domains were calculated. To provide estimates of bivariate associations, we calculated Pearson correlations between the eight stress domains and alcohol use frequency. Further, we conducted multiple multinomial logistic regression models to examine the effect of each stress domain on alcohol use frequency with contrast between 1 and 3 times and no use, 4 or more times and no use, and 4 or more times and 1–3 times, while adjusting for the other domains. The models were also adjusted for gender, age, parental nativity, and dominant language. We employed likelihood ratio tests to compare the nested multinomial logistic regression models that included a block of interaction variables versus models without interaction variables. We tested the moderation effect of gender (male vs. female) and age (centered on mean) on the relation between stress domains and alcohol use frequencies. Statistically significant interactions terms indicated a possible moderation effect, though the decision to test these indicators was made a posteriori.
To address potential biases related to missing data, we conducted sensitivity analyses using multiple imputation. Multivariate imputation using fully conditional specification methods was employed to generate 20 datasets with complete information for all HSI-A measures and covariates. Model estimates from these 20 imputed datasets were combined to obtain a final set of parameter estimates. Fully conditional specification methods are commonly used to impute missing values for both continuous and categorical variables in a dataset with an arbitrary missing pattern (Graham, Olchowski, & Gilreath, 2007; Rubin, 1987; Schafer, 1997; Schafer & Olsen, 1998), which was the case in this study (appendix of arbitrary missing pattern available on request). Because results based on imputed and nonimputed data did not differ substantially, we present only results based on imputed data. All analyses were conducted using SAS version 9.3.
Results Descriptive Statistics and Bivariate Associations
During the previous 30 days, 75.8% (n = 683) of the sample reported no use and 24.2% (n = 218) reported alcohol use. Among adolescents who reported using alcohol, 64.2% (n = 111) reported using alcohol 1 to 3 times and 35.8% (n = 63) reported using alcohol 4 or more times. The proportion of adolescents who reported alcohol use were similar for male (22.0%) and female participants (25.9%), χ2(1) = 1.80, p = .18. Alcohol varied significantly by age, t(899) = −6.34, p < .001; adolescents who reported using alcohol during the previous 30 days had a mean age of 15.54, compared with a mean age of 14.66 for youths who had not used alcohol during that period. Differences in alcohol use were found based on adolescents’ primary language, χ2(2) = 7.43, p = .02. Alcohol use was estimated to be the highest among adolescents whose primary language was English (28.8%), compared with those whose primary language was Spanish (18.5%) or other (3.3%). The bivariate association between alcohol use and parental nativity was not statistically significant (p = .257).
Six of the eight domains of stress were associated with increased alcohol use frequency during the previous 30 days, r = .07 to .18, p < .05. The immigration stress and family immigration stress subscales were not associated with alcohol use frequency. As expected, the eight domains of stress were correlated with one another, r = .11 to .48, p < .05, with the exception of community and gang violence and immigration stress, r = .06, p > .05. Tables are available on request.
Results of several independent t tests showing differences in the eight domains of Hispanic stress by alcohol use are also presented in Table 1. Significant differences in stress were observed for five of the eight domains. Significant mean differences between adolescents who used alcohol and those who did not were observed for acculturation gap (1.52 vs. 1.29, respectively), community and gang violence (1.32 vs. 1.17), family economic (1.32 vs. 1.21), discrimination (1.20 vs. 1.12), and family and drug-related (1.46 vs. 1.20) stress subscales. Higher mean scores were observed among youths who reported alcohol use during the previous 30 days compared with participants who reported no use. We examined the effect sizes of these differences, using Cohen’s d calculations, and found the greatest effect sizes in the family and drug-related (.53), acculturative gap (.44), and community and gang violence (.39) subscales. Much smaller effect sizes were observed in the family economic (.24) and discrimination (.18) subscales.
Mean HSI-A Stress Domain Scores Stratified by the Total Sample and Alcohol Use During the Previous 30 Days Among Hispanic Adolescents
Multinomial Logistic Regression
Multinomial logistic regressions were conducted to examine the relation between domains of Hispanic stress and alcohol use, adjusting for the effects of age, gender, dominant language, and parental nativity. Finding presented in Table 2 show that when adjusting for one another, only a few of the eight domains of Hispanic stress were associated with increased risk of alcohol use during the previous 30 days. Compared with those with lower community and gang violence stress, youths with higher stress in this domain had significantly greater relative risk (RR) of drinking 1 to 3 times (RR = 1.85, 95% CI [1.08, 3.17]) or 4 or more times (RR = 3.15, 95% CI [1.69, 5.90]) than not drinking at all during the previous 30 days. Additionally, adolescents with higher family and drug-related stress had a greater risk of drinking 1 to 3 times (RR = 2.06, 95% CI [1.37, 3.09]) compared with not drinking at all. However, increased family and drug-related stress was associated with lower risk of drinking 4 or more times (RR = 0.50, 95% CI [0.27, 0.90]) than drinking 1 to 3 times. Higher acculturative gap stress was associated with greater risk of drinking 4 or more times (RR = 2.39, 95% CI [1.40, 4.09]) than those who reported not drinking. No other stress domains were significantly associated with increased risk of alcohol use during the previous 30 days. These findings suggest that after adjusting for other stress domains, acculturation gap, community and gang violence, and family and drug-related stressors were important factors associated with alcohol use beyond the other domains of Hispanic stress included in the model. Finally, results indicated neither gender nor age moderated the relation between Hispanic stress and alcohol use frequencies (p > .05).
Relative Risks (RRs) and 95% Confidence Intervals (CIs) for Alcohol Use Frequency During the Previous 30 Daysa
DiscussionOur study is one of the first to use a standardized multidomain measure of Hispanic stress to understand alcohol use among Hispanic adolescents in the United States. The HSI-A measures eight domains of stress developed specifically for Hispanic adolescents to provide a better understanding of how these experiences may be related to alcohol use. Most adolescents in the sample who reported drinking in the last 30 days reported lower use; 64.2% (n = 111) reported using alcohol 1 to 3 times and 35.8% (n = 63) reported using alcohol 4 or more times during the previous 30 days. Youths in our study reported lower use of alcohol than in national samples. Whereas 24.2% (n = 218) of our sample reported alcohol use during the previous 30 days, 34.9% of high school youths nationally and 37.5% of Hispanic high school youths nationally report alcohol consumption during the prior month (Kann et al., 2014). However, it should be noted, the highest proportion of alcohol use is in older youth in eleventh (39.2%) and twelfth (46.8%) grade. Lower alcohol use was reported in tenth (30.9%) and ninth graders (24.4%). The lower rates of use in our sample may be due to the inclusion of younger adolescents in our sample (i.e., 12- and 13-year-olds), as the mean age in our sample was 14.87 (CDC, 2014). The alcohol use in our sample is closer to national proportions of use in younger youth.
Our analysis found evidence of a significant relation between Hispanic stress and adolescent alcohol use. Compared with those who did not drink, adolescents who reported using alcohol had significantly higher scores on family economic, culture and educational, acculturative gap, discrimination, community and gang violence, and family and drug-related stress subscales. Moderate effect sizes were observed between alcohol use and family and drug-related, acculturative gap, and community and gang violence stress, with small effect sizes found between alcohol use and family economic and discrimination stress. Our multivariate analyses also found community and gang violence, acculturative gap, and family and drug-related stress to be associated with alcohol use patterns beyond the influence of other cultural stressors; thus, we focused our discussion on these three significant correlates of alcohol use.
Community and Gang Violence Stress
In our study, community and gang violence stress was associated with both moderate and high levels of drinking. General population studies have explored neighborhood effects on adolescent drinking (Chuang, Ennett, Bauman, & Foshee, 2005). For example, living in environments with lower socioeconomic status is associated with increased peer drinking and adolescent alcohol use. The lower socioeconomic circumstances found in many predominately Hispanic neighborhoods have been associated with greater exposure to gangs, drug abuse, and discrimination (Cervantes et al., 2008). This neighborhood disadvantage has been associated with a lack of social and economic resources and opportunity (Sampson, Raudenbush, & Earls, 1997). Further, exposure to neighborhood crime and perceptions of crime and violence are associated with alcohol and substance use (Boardman, Greenberg, Vining, & Weimer, 2001; Duncan, Duncan, Strycker, & Chaumeton, 2002).
Given the association between community gang stress and alcohol use in our sample, the effect of the neighborhood context on Hispanic youth alcohol use should be examined further in future research. For example, our findings align with previous research indicating a strong correlation among economic circumstances, crime, alcohol and substance use, and negative health outcomes (Galea & Vlahov, 2002; Glaeser, Sacerdote, & Scheinkman, 1996), and minority groups are disproportionately affected in large part due to long histories of oppression and segregation that put them at higher risk of poverty (Galea & Vlahov, 2002). Structural approaches addressing these social conditions may help in alleviating the stress response (Dickerson & Kemeny, 2004) and reducing alcohol use among Hispanic adolescents.
Acculturative Gap
Higher acculturative stress was associated with a greater risk of drinking 4 or more times compared with those that reported no alcohol use during the previous 30 days. A commonly explored risk factor for alcohol use among Hispanics is acculturative stress. The experience of immigration is stressful (Thomas, 1995) and studies have reported an association between alcohol use and acculturation processes among adults (Johnson, 1996). However, findings have been inconsistent regarding the relation between acculturative stress and alcohol use during adolescence, with studies finding both positive and negative associations (Cabassa, 2003; Rogler, Cortes, & Malgady, 1991; Vega, Alderete, Kolody, & Aguilar-Gaxiola, 1998). Some studies have indicated that lower acculturation is associated with alcohol use among Hispanic boys but not girls (Epstein, Griffin, & Botvin, 2000).
The inconsistent findings of these previous studies may be due to the complex nature of acculturation for adolescents and the challenge of measuring acculturative stress—a complex and multidimensional process. For example, acculturation during adolescence is more interactive with the family; the stress of acculturating at a different pace than parents may cause parent–adolescent conflict (Patterson, Reid, & Dishion, 1992; Szapocznik & Williams, 2000). When parents and adolescents acculturate at a difference pace (i.e., acculturative gap or differential acculturation), this can increase family conflict and decrease family cohesion (Hwang & Wood, 2009; Szapocznik & Williams, 2000). Differential acculturation has been associated with increased mental health problems (Vega, Khoury, Zimmerman, Gil, & Warheit, 1995) and alcohol use among Hispanic adolescents (e.g., Martinez & Eddy, 2005; Santisteban, Muir-Malcolm, Mitrani, & Szapocznik, 2002), and our study further supports its status as a salient mechanism related to alcohol use in this population.
Family and Drug-Related Stress
Similar to the salient finding of acculturative gap stress, the current study found that another family related construct (family and drug-related stress) was associated with a greater risk of drinking 1 to 3 times compared to not drinking at all. However, increased family and drug-related stress was associated with a lower risk of drinking 4 or more times than drinking 1 to 3 times. Finding an association between these constructs was not unexpected; the population literature has suggested that youths are more likely to use alcohol when their families have norms that promote drinking (Song, Smiler, Wagoner, & Wolfson, 2012). However, given the importance of familismo, which stresses the centrality of family and adherence to familial values and norms (Galanti, 2003), the use of substances by parents may be especially relevant to Hispanic adolescent substance use.
Although we hypothesized (and found) an effect of family and drug-related stress on alcohol use, findings by level of alcohol use were unexpected. Contrary to our initial hypothesis, family and drug-related stress was lower among adolescents who reported drinking 4 or more times during the previous 30 days as compared with those who reported drinking 1 to 3 times. It is possible that this finding is an anomaly and may be better understood if we had a measure of consumption (e.g., binge drinking), rather than only being able to report on incidence. There is some research showing that Hispanic’s are less likely to drink. However when they do drink, binge use is more common (SAMHSA, 2010; Borges et al., 2006).We were unable to explore this in the present dataset but acknowledge that future research could shed light on this contradictory finding. Nevertheless, our study indicates that youths reporting family and drug-related stress are engaging in low to moderate alcohol use and any alcohol use in adolescence is associated with heavier use later in life (Stueve & O’Donnell, 2005). This underscores again the importance of family in the context of Hispanic adolescent drinking.
Gender and Age Effects
The most recent Youth Risk Behavior Survey (Kann et al., 2014) found no differences by males and females in alcohol consumption during the previous 30 days nationally (35.5% vs. 34.4%, respectively). Some previous research, however, has suggested that the relation between stress and alcohol use may be different for boys and girls (Epstein, Botvin, & Diaz, 2000; Oshri et al., 2014). For Hispanic adolescents, less is known about the relation among stress, gender, and alcohol use. We explored whether different Hispanic stressors might be more or less salient to boys and girls in the context of alcohol use. In our sample, no gender differences were found between HSI-A stressors and alcohol use. This suggests that these stressors have a similar effect on drinking patterns among youths regardless of gender.
Likewise, age is often a significant predictor of alcohol use during adolescence, with older youths reporting drinking more frequently (Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2014). Given the documented association between age and alcohol use in the literature, we explored whether the relation of stress to alcohol use in Hispanic adolescents was moderated by age. Consistent with previous research, age was related to alcohol use, with a significantly higher mean age in the group that reported alcohol use during the previous 30 days. However, our analyses found that age did not moderate the relation between stress and alcohol use; in other words, the association between stress domains and alcohol use was not different for younger versus older youths. Thus, we found that despite developmental changes as youths grow older, the stressors associated with alcohol use do not seem to become more or less relevant to alcohol use based on age.
Limitations and Conclusions
Despite its strengths, the current study is not without limitations. Issues associated with sampling, measurement, and research design should be noted and considered in both the interpretation of our findings and the development of future research. Although the sample was large, schools were not randomly selected. It is possible that youths who engage in high-risk alcohol use are less likely to attend school and were therefore underrepresented in this sample. Additionally, only about one third of students nationally are eligible for free or reduced-price lunches (National Center for Education Statistics, 2005), whereas more than 50% of students at all study sites in the current sample were eligible (suggesting lower socioeconomic status). Thus, we may not be fully representing the experience of Hispanic adolescents nationally. Additionally, alcohol use is typically assessed by national agencies such as SAMHSA in the same fashion as this study, but additional measures of binge drinking may have revealed differences in both frequency and intensity of drinking. Although drinking more than 4 times during a month was suggestive of using alcohol at least once a week, which is problematic given that the sample is underage (Chou & Pickering, 1992), we could not determine if some youths in our sample drank once per week during the course of a month or four times in a row during a single weekend. Understanding differences among usual, episodic, and binge-drinking patterns may elucidate differences by stress domain. As with all cross-sectional data, determinations of causality were not possible. Although theory and previous research have suggested that Hispanic stressors cause adolescent substance use, at least in part, they may also have the opposite effect; adolescents experiencing distress related to their drinking patterns may be more likely to report these types of stressors. Thus, longitudinal designs are needed to investigate the direction of these effects.
Despite these limitations, our study lends support to the need for research on both the community context and families. Further, in our study, domains related to the family (acculturative gap and family and drug-related stress) and community and gang violence had the strongest effect sizes compared to all other domains. Previous literature has supported the importance of family to Hispanic youth development (Prado & Pantin, 2011) and the relation between family conflict and alcohol use patterns among Hispanic youths specifically; this may indicate the importance of including family variables in studies of substance use. These findings also have some application to intervention research. Several interventions have highlighted the role of family based prevention of adolescent substance use (e.g., Cervantes & Goldbach, 2012; Schwartz et al., 2013). Although our study found these domains to be the strongest correlates of alcohol use, nearly all (six of eight) of the domains were associated with drinking at the bivariate level. Thus, we would caution that interventions should address a diverse set of Hispanic stressors and further research should explore the most salient predictors of change in alcohol use. Although more research is needed, particularly longitudinal and experimental studies, the relation between these eight domains of Hispanic stress and alcohol use among adolescents provides a basis for identifying mechanisms of change that are unique to this high-need population.
Footnotes 1 The Hispanic stress items were asked in a two-part question. First, participants were asked if they experienced a specific stressor. If participants answered affirmatively, they were asked to appraise the stress experience on a scale of 1–5. The measure was constructed by combining negative responses with scores of 1 (not at all worried or tense) to maintain sample size. This was the coding process by which the measure was tested and validated.
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Submitted: January 2, 2015 Revised: September 15, 2015 Accepted: September 16, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 960-968)
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Record: 68- The relationship of therapeutic alliance and treatment delivery fidelity with treatment retention in a multisite trial of twelve-step facilitation. Campbell, Barbara K.; Guydish, Joseph; Le, Thao; Wells, Elizabeth A.; McCarty, Dennis; Psychology of Addictive Behaviors, Vol 29(1), Mar, 2015 pp. 106-113. Publisher: American Psychological Association; [Journal Article] Abstract: This study examined associations of therapeutic alliance and treatment delivery fidelity with treatment retention in Stimulant Abusers to Engage in Twelve-Step (STAGE-12), a community-based trial of 12-Step Facilitation (TSF) conducted within the National Drug Abuse Treatment Clinical Trials Network (CTN). The STAGE-12 trial randomized 234 stimulant abusers enrolled in 10 outpatient drug treatment programs to an eight-session, group and individual TSF intervention. During the study, TSF participants rated therapeutic alliance using the Helping Alliance questionnaire-II. After the study, independent raters evaluated treatment delivery fidelity of all TSF sessions on adherence, competence, and therapist empathy. Poisson regression modeling examined relationships of treatment delivery fidelity and therapeutic alliance with treatment retention (measured by number of sessions attended) for 174 participants with complete fidelity and alliance data. Therapeutic alliance (p = .005) and therapist competence (p = .010) were significantly associated with better treatment retention. Therapist adherence was associated with poorer retention in a nonsignificant trend (p = .061). In conclusion, stronger therapeutic alliance and higher therapist competence in the delivery of a TSF intervention were associated with better treatment retention whereas treatment adherence was not. Training and fidelity monitoring of TSF should focus on general therapist skills and therapeutic alliance development to maximize treatment retention. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Relationship of Therapeutic Alliance and Treatment Delivery Fidelity With Treatment Retention in a Multisite Trial of Twelve-Step Facilitation
By: Barbara K. Campbell
Department of Public Health and Preventive Medicine, Oregon Health and Science University;
Joseph Guydish
Philip R. Lee Institute for Health Policy Studies, University of California–San Francisco
Thao Le
Philip R. Lee Institute for Health Policy Studies, University of California–San Francisco
Elizabeth A. Wells
School of Social Work, University of Washington
Dennis McCarty
Department of Public Health and Preventive Medicine, Oregon Health and Science University
Acknowledgement: Awards from the National Institute on Drug Abuse (R01 DA025600, U10 DA015815 and P50 DA009253) supported this investigation. The authors appreciate the generous assistance of Dennis Donovan, Dennis Daley, and the STAGE-12 study team in providing access to STAGE-12 study data. The authors also thank Kevin Delucchi, University of California–San Francisco, for consultation regarding statistical analysis and Emma Passalacqua for her formatting assistance. Special thanks to the raters and the staff at all of the participating treatment sites.
Longer stays and better attendance are associated with enhanced outcomes in substance abuse treatment (Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997; Simpson, 1981; Zhang, Friedmann, & Gerstein, 2003). However, treatment retention remains a challenge, and premature termination remains a common problem (Brorson, Arnevik, Rand-Hendriksen, & Duckert, 2013; Stark, 1992; Swift & Greenberg, 2012). Research has typically examined patient characteristics associated with retention or, conversely, with dropout. However, few patient characteristics show consistent associations with treatment dropout in either addictions treatment (Brorson et al., 2013) or the broader psychotherapy domain (Swift & Greenberg, 2012). Reviews note the lack of research on the relationship of treatment variables with retention and recommend that this area be addressed (Brorson et al., 2013; Swift & Greenberg, 2012). Among the limited studies of treatment variables associated with retention, manualized and time-limited treatments (Swift & Greenberg, 2012) and higher therapeutic alliance (Brorson et al., 2013; Sharf, Primavera, & Diener, 2010) have been associated with better retention.
Treatment Retention and Therapeutic AllianceTherapeutic alliance, the collaborative relationship between therapist and patient, has been conceptualized as a common treatment factor present across treatment orientations (Horvath & Luborsky, 1993). It has predicted attendance (Fiorentine, Nakashima, & Anglin, 1999; Simpson et al., 1997), retention (De Weert-Van Oene, Schippers, De Jong, & Schrijvers, 2001; Meier, Donmall, McElduff, Barrowclough, & Heller, 2006; Knuuttila, Kuusisto, Saarnio, & Nummi, 2012; Ruglass et al., 2012), and outcomes (Connors, Carroll, DiClemente, Longabaugh, & Donovan, 1997; Gillaspy, Wright, Campbell, Stokes, & Adinoff, 2002; Crits-Christoph, Johnson, Connolly Gibbons, & Gallop, 2013) for patients in substance abuse treatment. Early therapeutic alliance, is particularly associated with treatment engagement and retention (Meier et al., 2006; Brorson et al., 2013). This robust finding supports focusing retention research on treatment variables, including common and specific treatment factors. To do so, it may be useful to investigate the degree to which delivery of specified treatments as intended, known as treatment delivery fidelity, is associated with retention.
Treatment Retention and FidelityTreatment delivery fidelity has received considerable research attention as a method of ensuring internal trial validity (Bellg et al., 2004; Gearing et al., 2011). Fidelity measures most commonly involve observer ratings of adherence to specified treatment content as well as ratings of therapist competence or skill delivering the treatment (Borrelli, 2011). Research on the relationship of therapist adherence and competence to patient outcomes in substance abuse treatment has produced mixed findings (Webb, DeRubeis, & Barber, 2010). Some studies have only identified fidelity-outcome relationships after controlling for the effects of therapeutic alliance (Barber et al., 2006; Gibbons et al., 2010; Hogue et al., 2008). Although numerous studies have investigated fidelity-outcome relationships, we identified only one study that examined the relationships between therapist adherence and competence and treatment retention in substance abuse treatment. The study found no significant association between adherence to a three-session motivational interviewing (MI) intervention and days of outpatient treatment enrollment, whereas competence in advanced MI skills (measured using the entire therapist sample, including therapists conducting treatment as usual [TAU]) was negatively associated with retention in outpatient treatment at 4-weeks postintervention (Martino, Ball, Nich, Frankforter, & Carroll, 2008). There were no significant competence-retention relationships within the MI therapist-only sample. Unexpected competence-retention results are difficult to understand, particularly given the significant effects often shown for MI in increasing treatment retention (Hettema, Steele & Miller, 2005). This may be a spurious result due to the relatively high number of analyses conducted in the study. An alternative explanation may be that clients at risk for treatment disengagement prompted therapists to use motivational strategies with greater skill in an effort to build motivation for treatment (S. Martino, personal communication, April 2014). Results point to the need for further research to clarify relationships among adherence, competence, and retention in treatments for substance use disorders. Fidelity-retention relationships should be studied across different manual-guided treatments, particularly given the finding that overall retention rates have been found to be superior for manualized versus nonmanualized treatments (Swift & Greenberg, 2012).
Treatment Retention and Outcomes in 12-Step FacilitationTwelve-Step Facilitation (TSF) is a manual-guided treatment for alcohol and substance use disorders that seeks to increase clients’ engagement in 12-Step activities outside of formal treatment sessions. Since Project Match, which found TSF to be comparable in outcomes to MI and cognitive–behavioral therapy (Project Match Research Group, 1997), empirical support for TSF has accumulated (Brown, Seraganian, Tremblay, & Annis, 2002; Carroll, Nich, Ball, McCance, & Rounsaville, 1998; Kaskutas, Subbaraman, Witbrodt, & Zemore, 2009; Timko, DeBenedetti, & Billow, 2006; Timko & DeBenedetti, 2007). Research to date has shown that retention in TSF has generally been comparable with other treatments (Carroll et al., 1998; Project Match Research Group, 1997) and that retention in TSF is associated with better outcomes. (Kaskutas et al., 2009; Timko, Sutkowi, Cronkite, Makin-Byrd & Moos, 2011).
Stimulant Abusers to Engage in Twelve Step (STAGE-12), the parent study for the current analysis, examined the efficacy/effectiveness of TSF for stimulant abusers conducted in community treatment programs (Donovan et al., 2013). The study trained outpatient counselors in 10 treatment centers to deliver a group-plus-individual TSF treatment that was integrated into TAU and compared with a TAU-only condition. TSF retention was comparable to TAU retention as measured by self-reported, group session attendance within a 30-day period and was higher for the number of individual sessions reported by participants. TSF participants had a higher likelihood of abstinence from stimulants during treatment, although there were no differences at follow-up. TSF participants had higher rates of attendance and involvement in 12-Step programs posttreatment and at 6-month follow-up. The relationship of number of TSF treatment sessions attended (using a dichotomous measure called high vs. low exposure) to participant outcomes was also examined (Wells et al., in press). High exposure to treatment, defined as attendance at two or more (out of three) individual sessions plus three or more (out of five) group sessions, was achieved by 77% of TSF participants and was associated with (a) significantly higher odds of abstinence from stimulants during treatment and across 4 months of follow-up; (b) significantly lower rates of stimulant use for nonabstinent participants and nonstimulant drug use during treatment, but not after; and (c) more days of attending 12-Step meetings and engaging in duties during meetings through 90-days posttreatment (Wells et al., 2014).
In an ancillary study, we assessed the reliability and concurrent validity of the Twelve Step Facilitation Adherence Competence Empathy Scales (TSF ACES), a ratings measure of treatment delivery fidelity based on an expansion of the adherence rating scales used in STAGE-12 (Campbell, Manuel, et al., 2013). Trained, independent raters evaluated the fidelity of all audio-recorded TSF sessions. The availability of comprehensive fidelity ratings for the entire TSF sample provided an opportunity to study relationships of fidelity with other variables, including predictors and outcomes. A prior report (Campbell, Buti, et al., 2013) found that therapists reporting self-efficacy in basic counseling skills had higher adherence, competence, and empathy delivering the TSF intervention and those with graduate degrees had higher adherence. In contrast, therapists with more positive attitudes toward 12-Step groups and self-efficacy in addiction-specific counseling skills had lower adherence ratings. In a study of fidelity-patient outcomes relationships, greater therapist empathy was significantly associated with fewer days of self-reported drug use at 3-months posttreatment; greater competence was associated with this outcome in a nonsignificant trend (p = .06), and there was no association of adherence with days of drug use. All three fidelity measures were associated with better employment outcomes on the Addiction Severity Index (ASI) but worse drug composite scores at 3-months posttreatment. Analysis of ASI drug use items, which include days of use and how troubled the respondent is by use, showed that greater fidelity was associated with fewer days of use but an increased sense of being troubled by use (Guydish et al., 2014). The authors noted that different types of ASI items had different relationships to the same predictor and posited that maxims such as “one day at a time” kept the risk of drug use at the fore even as actual drug use declined. The current study examined the relationships of treatment retention in STAGE-12 TSF, as measured by number of sessions attended, with treatment delivery fidelity (i.e., therapist adherence, competence, and empathy) and therapeutic alliance, as reported by participants at the second treatment visit.
Method Overview of STAGE-12 Trial
STAGE-12 was a multisite, randomized trial conducted in 10 outpatient community treatment centers across the United States. Patient participants were adults with stimulant abuse/dependence who were enrolled or seeking treatment admission. Participants were randomly assigned to either STAGE-12 TSF (i.e., five-session group plus three-session, individual, intensive referral sessions) plus TAU (N = 234) or TAU (TAU; 5–15 hours of weekly treatment; N = 237). The STAGE-12 study was approved by the University of Washington Institutional Review Board (IRB) as well as the IRBs of all academic institutions affiliated with participating sites. See Donovan et al. (2013) for a complete description of STAGE-12 study participants, procedures, and the TSF treatment.
Participants
The STAGE-12 trial randomized 234 participants to the TSF intervention. Two hundred (85.5%) completed the therapeutic alliance measure, Helping Alliance questionnaire-II (HAq-II; Luborsky et al., 1996), at 2 weeks postrandomization. We excluded the following because of incomplete measures: (a) four who did not have fidelity ratings; (b) one who completed the HAq-II before attending any treatment sessions, thus invalidating the measure; and (c) six who had more than 20% missing HAq-II items (i.e., four or more items). We also excluded 15 participants who had missing baseline ASI alcohol composite or ASI drug composite scores. Our analysis included 174 participants who met the following conditions: completed HAq-II at 2 weeks postrandomization and attended at least one session to produce a valid measure of therapeutic alliance, were rated for treatment fidelity, had completed at least 80% of HAq-II items, and had ASI alcohol and drug composite scores at baseline.
Study Therapists
All therapists (N = 106) at study sites were considered for inclusion based on four eligibility criteria: (a) credentialed to provide substance abuse treatment, (b) approved by the treatment program’s administration, (c) willing to participate and to be randomized, and (d) familiar with the 12-Step orientation. There were 39 therapists (37%) who met criteria and were included in the study pool; 2 from each site were chosen at random from the pool to conduct the TSF treatment. The remaining therapists were available for training as replacement therapists, four of who became TSF therapists. Supervisors were trained as backup TSF therapists. In total, there were 34 therapists and supervisors who conducted the TSF intervention. We obtained demographic information from 33 of the 34 therapists; they were predominantly Caucasian (70%) women (67%) with a mean age of 52 years (SD = 9.2). Most (82%) had at least 5 years of counseling experience and 55% had masters’ degrees or above. During the trial, therapists were trained in the TSF intervention, certified and monitored for adherence by on-site supervisors and expert raters (four clinicians experienced in substance abuse treatment and trained in the TSF intervention: one masters’ level, one doctoral candidate, and two doctoral level). The STAGE-12 trial audio-recorded all TSF sessions but did not record TAU sessions.
Independent Fidelity Raters
We recruited separate raters from local graduate programs to conduct ratings of all audio-recorded, STAGE-12 TSF sessions. The nine raters (seven with masters’ degrees and two with doctoral degrees) averaged 5 years of clinical experience (SD = 4.05), 7 years of research experience (SD = 5.96), and 1 year of rating experience (SD = 2.95). A doctoral level psychologist with extensive experience in fidelity monitoring served as an expert rater and trainer/ratings supervisor.
Measures
TSF ACES
TSF ACES measures five dimensions of fidelity using 6-point scales, three of which were used in our analysis: (a) adherence—delivery of specific treatment content; (b) competence—the skill of content delivery; and (c) global empathy—the therapist’s effort to understand the clients’ perspectives (adapted from the MI Treatment Integrity scale; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005). There are four content rating forms, one for group sessions (10 items) and three corresponding to STAGE-12 individual Session 1 (10 items), Session 2 (4–5 items), and Session 3 (8–9 items). Summary measures derived for each session had modest to excellent inter-rater reliabilities, with intraclass correlations of .91 for mean adherence, .90 for mean competence, and .69 for global empathy. Internal consistencies computed with Cronbach’s α for summary measures based on multiple items were .69 for mean adherence and .71 for mean competence. In assessing TSF ACES convergent validity with the HAq-II, all correlations were in the expected directions (e.g., negative correlation of HAq-II with proscribed therapist behaviors); there were no significant correlations for mean adherence, mean competence, or global empathy with HAq-II scores collected at week 2. See Campbell, Manuel, et al. (2013) for a further description of the psychometric characteristics of the ratings scale and for sample items. The TSF ACES ratings manual and forms are available at http://ctndisseminationlibrary.org/PDF/795_TSFACES.pdf.
HAq-II
Therapeutic alliance was assessed using the patient version of the HAq-II (Luborsky et al., 1996). This self-report measure assesses the degree to which patients experience therapist and treatment as collaborative and helpful. The HAq-II had good test–retest reliability (.78), internal consistency (.90), and convergent validity on a normative sample of cocaine abusers (Luborsky et al., 1996), and it is frequently used in alliance research with substance-abusing samples (see Meier, Barrowclough, & Donmall, 2005 for a review). The instrument contains 19 items measured on a 6-point Likert scale; the sum of the items (with negative items reverse scored) forms the total score. STAGE-12 study participants completed the HAq-II at week 2 of treatment and week 8 (i.e., end of treatment). We used week 2 scores for our analysis to use a measure that temporally preceded our outcome measure and based on previous robust findings of early alliance predicting engagement and retention.
ASI-Lite
The ASI-Lite (McLellan et al., 1992) was administered at baseline and follow-up in STAGE-12. ASI composite scores measure problem severity in seven areas (medical, employment, legal, alcohol, drug, social, psychological; McLellan et al., 1985). Scores are derived from questions in each area measuring problem severity within the prior 30-day period. We used baseline ASI drug and alcohol composite scores as measures of substance use severity at treatment entry.
Treatment Retention
The number of TSF sessions attended (ranging from 0 to 8) was the measure chosen for treatment retention. Session attendance was reviewed and recorded weekly during the treatment phase by the therapist.
Procedures for Independent Fidelity Ratings
Raters viewed the STAGE-12 TSF therapist training video and completed a 1-day training. Before rating study sessions, raters achieved a criterion level of inter-rater reliability with the ratings expert on audio-recorded practice sessions conducted by STAGE-12 therapists. Audio-recordings of all TSF group (n = 512) and individual (n = 487) sessions were randomly assigned to certified raters in sets of 20; one session per set was randomly assigned to the study expert for co-rating to monitor ratings consistency. There were 33 incomplete or poor-quality audio-recordings, leaving 966 rated sessions. The University of California–San Francisco and Oregon Health and Science University IRBs approved the procedures for the fidelity study. See Campbell, Buti, et al., (2013) for more detail.
Data Analysis
Descriptive statistics, including means, standard deviations, and percentages, were used to summarize characteristics of TSF participants at baseline. Comparisons between TSF participants included in our analysis and those excluded from analysis due to missing data were conducted with t tests for continuous variables and with χ2 tests for categorical variables.
We first tested univariate associations of therapeutic alliance and treatment delivery fidelity (mean adherence, mean competence, mean empathy) with treatment retention (i.e., number of sessions attended). Poisson regression modeling examined the multivariate relationship between these predictor variables and the retention outcome measure. Age, gender, race, and baseline values of ASI drug and alcohol composite scores were included in the Poisson model. Nesting of clients within site was controlled for as well. Nesting of clients within counselor was not controlled because clients received STAGE-12 group sessions from more than one counselor. Analyses were conducted using SAS software version 9.3 (SAS, Inc., Cary, NC).
Results Participant Characteristics
The mean age of participants in the analytic sample was 38.1 (SD = 10.2) years and 62% were women. White participants accounted for 44% of the sample, and African Americans accounted for 37%. More than half (52.6%) had never married, 23.7% were divorced, and 14.5% reported being married. Approximately half were high-school graduates, and 29% had some college education. Most were working (35.1% full time and 23.6% part time). Participants included in the analyses (n = 174) were similar to those excluded (n = 60) on these demographic characteristics. However, participants included in the analysis received significantly more STAGE-12 sessions (M = 5.6, SD = 2.0) than those not included in the analysis (M = 2.2, SD = 2.3, p < .001), primarily because of the inclusion criterion of having completed a HAq-II after attending at least one session. See Table 1 for participant characteristics.
Baseline Demographic Characteristics for STAGE-12 TSF Participants Included (n = 174) and Not Included (n = 60) in the Analysis
Number of Sessions Attended
See Table 2 for a distribution of session attendance for the 174 participants included in our analysis. Approximately 5% attended only one session, whereas 14% attended all eight sessions. The mean number of sessions was 5.6 (SD = 2); most participants (88%) attended 4–8 sessions.
Distribution of Number of TSF Counseling Sessions Attended
Relationship Among Therapeutic Alliance, Treatment Fidelity, and Treatment Retention
Results of univariate and multivariate analyses are shown in Table 3. In the univariate analysis, there was a statistically significant association between therapeutic alliance (HAq-II) and treatment retention (β = 0.142, p = .002) and no significant relationships of fidelity variables with retention. In the multivariate analysis, controlling for age, gender, race, baseline ASI drug composite, baseline ASI alcohol composite, adherence, competence, and empathy scores, an increase in therapeutic alliance by one unit resulted in an increase in the number of sessions attended by 14% (exp(β) = 1.14; p = .005). Likewise, there was a significant association of therapist mean competence with retention; an increase in therapist mean competence by one unit resulted in an increase in session attendance by 36% (exp(β) = 1.36; p = .010). The association between number of sessions attended and mean adherence, while controlling for all other variables, approached significance (p = .061); for every increase of one unit in mean adherence, session attendance decreased by 20% (exp(β) = 0.80). There was no significant association between session attendance and empathy scores when controlling for other variables, and none of the patient-characteristic control variables were associated with retention.
Parameter Estimates (β), Exponential β (95% CI) and p Values of Univariate and Multivariate Analysis Examining Associations Among Therapeutic Alliance, TSF Fidelity Predictors, and Treatment Retention (n = 174)
DiscussionThe robust association of longer retention with better outcomes in substance abuse treatment has been extended to TSF treatment (Kaskutas et al., 2009; Wells et al., 2014), supporting the importance of treatment retention for TSF. The current study contributes to recommended research on treatment variables as predictors of retention (Brorson et al., 2013; Swift & Greenberg, 2012). Our findings indicated that early, participant-rated, therapeutic alliance was significantly associated with retention in TSF in univariate and multivariate analyses. To our knowledge, it is the first study to show a relationship of therapeutic alliance with retention in TSF with substance abusers and corroborates a previous finding from Project Match showing a relationship of therapist-rated, therapeutic alliance with outpatient TSF retention for alcohol-dependent participants (Connors et al., 1997). Results are also the first to identify a significant fidelity-retention relationship for manual-guided TSF treatment, a finding that has important implications for treatment delivery. Therapist competence was associated with higher session attendance when therapeutic alliance and other fidelity variables were controlled. Unexpectedly, the multivariate model suggested a relationship between higher adherence and poorer retention, that, although not significant in this analysis (p = .061), may bear additional attention in future research.
Results suggest that variables related to general therapist skill, which facilitate development of positive therapeutic alliance and are associated with competent TSF delivery, may improve attendance more than strict intervention adherence. Results are consistent with findings from Guydish et al. (2014) indicating that therapist empathy and competence were associated with better patient outcomes whereas adherence was not, although the lack of a significant empathy finding in the current study is inconsistent with this pattern of results. It may be that the relationship of empathy with retention is accounted for mostly in facilitating therapeutic alliance, such that, when alliance is controlled, differences in therapist empathy do not affect retention. Overall, findings lend support to the “common factors” (Castonguay, 1993) hypothesis regarding treatment effectiveness, suggesting that variables present across treatments, such as a positive therapeutic alliance and competent delivery of treatment content, may be central to increasing retention and improving outcomes.
Competence-retention findings in the current study are compatible with research showing that more experienced therapists had lower dropout rates (Swift & Greenberg, 2012) and that more advice-giving was associated with worse outcomes in group counseling (Crits-Christoph et al., 2013). Swift and Greenberg (2012) suggested that more experienced therapists may be more responsive and have a greater relationship focus, which may explain their ability to retain clients in treatment. Therapist responsiveness to client presentation may also be relevant for adherence results. Adherence may provide intervention structure that ensures the inclusion of empirically supported practices. However, departures from strict adherence based on therapist responsiveness to changes in client presentation (i.e., therapist attunement) may improve alliance, address client need more effectively, and appropriately individualize treatment in community settings serving heterogeneous clients with multiple comorbidities. It has been argued that flexible application of manual-guided treatments, including training about when and how to be flexible, optimizes the use of empirically supported treatments in clinical practice (Kendall, Gosch, Furr, & Sood, 2008). Use of a mean adherence measure in our study may have obscured the precise adherence information needed to show a relationship with retention. If the therapist responsiveness (i.e., flexible fidelity) hypothesis is correct, then variations in strict adherence based on therapist-client interactions may be associated with improved retention and better outcomes and may require more finely tuned measurement.
Use of fidelity ratings of TSF sessions, using an instrument with known psychometric properties (Campbell, Manuel, et al., 2013) and independent raters who had undergone rigorous training, are strengths of the current study. The inclusion of a measure of therapeutic alliance is also a strength, not only to assess its relationship with retention but also as a variable to control when examining fidelity-retention relationships. Missing data that eliminated approximately 25% of TSF participants from the current analysis are a study weakness, although the excluded sample did not differ demographically from the sample included in our analysis. Participants who did not attend any sessions did not have fidelity or valid therapeutic alliance data, thus they were omitted from our analysis. This is a study limitation that prohibits us from identifying any variables associated with immediate dropout, an important treatment consideration. Lack of measurement of early symptom improvement among participants may also be considered a study limitation. Early participant improvement may be confounded with therapeutic alliance and a predictor of retention itself (Crits-Christoph, Connolly Gibbons, & Hearon, 2006; Webb et al., 2010). The use of a measure of therapeutic alliance after only week 2 of treatment may mitigate this concern given the limited time for improvement to occur. Also, limited research has shown that, although early alliance may be affected by symptom improvement, alliance remains a significant predictor of positive outcomes when early symptom improvement is controlled (Barber, Connolly, Crits-Christoph, Gladys, & Siqueland, 2000).
Recommendations
Clinicians should be trained and monitored in general therapy skills, not simply adherence, in clinical trials and community implementation of TSF and other behavioral interventions. This includes training designed to facilitate the therapeutic alliance, several interventions for which have been developed. Campbell et al. (2009) developed a brief intervention specifically designed to foster alliance development and found that it increased participants’ continuation in outpatient treatment after detoxification in a randomized trial. A preliminary study of alliance fostering therapy added to supportive-expressive therapy resulted in depressed patients’ reports of positive changes in quality of life (Crits-Christoph et al., 2006). The complex topic of training and intervention characteristics that facilitate alliance development and general therapist competence clearly requires further study. Studies should also examine therapist adherence variations during manual-guided treatment. Adherence flexibility may be a component of therapist competence that is superior to strict adherence, although this may vary depending on the specified treatment and client-related variables. Despite the need for further study, accumulating evidence, including the present study’s findings, suggests that training empirically supported treatments such as TSF should emphasize general therapist and alliance-developing skills to improve retention and outcomes.
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Submitted: January 29, 2014 Revised: April 16, 2014 Accepted: May 12, 2014
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Record: 69- The relationship of tobacco use with gambling problem severity and gambling treatment outcome. Odlaug, Brian L.; Stinchfield, Randy; Golberstein, Ezra; Grant, Jon E.; Psychology of Addictive Behaviors, Vol 27(3), Sep, 2013 pp. 696-704. Publisher: American Psychological Association; [Journal Article] Abstract: This study sought to examine the impact of tobacco use on gambling treatment. Pathological gambling (PG) is a psychiatric condition associated with significant financial, emotional, and psychosocial consequences. Elevated rates of nicotine dependence have been associated with increased gambling severity and more frequent psychiatric problems. A total of 385 treatment-seeking pathological gamblers enrolled in one of 11 gambling treatment providers in Minnesota were assessed. Linear regression modeling was used to examine demographic and clinical variables at treatment entry and the relationship between those variables and the number of days gambled at a 6-month posttreatment. Logistic regression was utilized to assess predictors of treatment completion. Daily tobacco use was reported in 244 (63.4%) subjects. Tobacco users presented with significantly more severe gambling and mental health symptoms at treatment intake. Daily tobacco use, however, was not significantly associated with the number of days gambled or with treatment completion. Although tobacco users present with greater gambling problem severity, they had similar rates of treatment completion and treatment outcomes as nonusers. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Relationship of Tobacco Use With Gambling Problem Severity and Gambling Treatment Outcome
By: Brian L. Odlaug
Department of Public Health, University of Copenhagen, Denmark;
Randy Stinchfield
Department of Psychiatry, University of Minnesota
Ezra Golberstein
School of Public Health, Division of Health Policy and Management, University of Minnesota
Jon E. Grant
Department of Psychiatry & Behavioral Neuroscience, University of Chicago
Acknowledgement:
Gambling is a popular and globally lucrative industry. Within the United States, $92.3 billion each year are wagered in casinos, racetracks, on sports, and lotteries (American Gaming Association, 2003). Concurrent with economic benefit, however, gambling is often associated with significant social problems, and research suggests that gambling may be much more costly than beneficial to a community. Significant societal costs, including credit card default, home foreclosures, delinquent bank loans, and medical costs are often associated with pathological gambling (PG; Walker & Barratt, 1999; Eadington, 2003; Petry, 2005).
Although many people gamble recreationally, the most recent epidemiological research indicates that between 0.42% to 0.6% of the United States population meets criteria for PG (Petry, Stinson, & Grant, 2005; Kessler et al., 2008), characterized by repetitive engagement in gambling that results in significant financial, occupational, and psychosocial dysfunction (National Opinion Research Center, 1999; American Psychiatric Association [APA], 2000). Although PG is commonly associated with other problematic behaviors, the most common co-occurring behavior is tobacco use. Among adults in the United States, 12.8% report nicotine dependence, and nicotine dependence is associated with higher rates of impulsivity (Upadhyaya, Deas, Brady, & Kruesi, 2002; Grant, Hasin, Chou, Stinson, & Dawson, 2004). Pathological gambling is associated with elevated rates of nicotine dependence (41% to 65%), and tobacco smoking in clinical samples of pathological gamblers has been associated with increased gambling severity and more frequent psychiatric problems (Smart & Ferris, 1996; Crockford & el-Guebaly, 1998; Stinchfield & Winters, 2001; Petry & Oncken, 2002; Grant & Potenza, 2005; McGrath & Barrett, 2009; Toneatto, Skinner, & Dragonetti, 2002; Grant, Desai, & Potenza, 2009; Grant, Kim, Odlaug, & Potenza, 2008; Shaffer et al., 1999; Potenza et al., 2004; Fagan et al., 2007). Although one small study (n = 35) found that tobacco use is associated with higher rates of relapse following cognitive–behavioral therapy (CBT; Grant, Donahue, Odlaug, & Kim, 2011), the impact of tobacco use on gambling treatment completion and outcomes is largely lacking from the literature. The identification of potential markers for the successful completion of gambling treatment would be valuable information for clinicians, public health officials, and patients, as the field strives to cost-effective and efficacious treatments. Given the emerging data indicating the possible negative effects of tobacco use on PG treatment outcome, an examination of daily tobacco use as a marker for gambling treatment completion and number of days gambled serves to expand the literature on behaviors influencing gambling treatment (Grant, Kim, Odlaug, & Potenza, 2008).
Gambling treatment involves a variety of programs, including inpatient, intensive outpatient, and individual and group CBT (Grant et al., 2009), and 12-step based support groups such as Gambler’s Anonymous (GA; Petry, 2003). Support groups such as GA, however, have reported poor overall outcomes, with studies indicating relapse rates of over 94% (Nathan, 2003; Slutske, 2006). Cognitive–behavioral approaches have generally yielded the most efficacious outcomes (Pallesen, Mitsem, Kvale, Johnsen, & Molde, 2005; Grant et al., 2009) although pharmacotherapeutic interventions have also demonstrated benefit for PG (Kim, 1998; Kim, Grant, Adson, & Shin, 2001; Grant, Kim, & Odlaug, 2007). A meta-analysis of 37 different treatment studies, however, found that individual or Group CBT was the most effective treatment in preventing relapse (Pallesen et al., 2005).
A variety of factors may influence treatment completion and abstinence from gambling. Comorbid addictions, including tobacco use, may be particularly important. Assessing the influence of daily tobacco use on treatment completion and gambling severity is prudent given the disproportionally high rates of nicotine use found in PG and the potential of co-occurring addictions to affect treatment outcomes (Grant & Potenza, 2005; Mooney, Odlaug, Kim, & Grant, 2011). In the area of alcohol treatment, a disorder with many clinical and possibly biological links to PG (Grant, Brewer, & Potenza, 2006), research suggests that co-occurring addictions may adversely affect treatment outcome (Winters & Kushner, 2003; Bobo, McIlvain, Lando, Walker, & Leed–Kelly, 1998). Studies in PG, however, have failed to examine the relationship of tobacco use on PG treatment completion and measures of gambling severity in large samples of individuals who seek an array of treatment options.
Based upon research indicating the potentially negative effects of nicotine use on treatment for gambling (Grant et al., 2011) or substance use disorders (Bobo et al., 1998), we hypothesize that those PG subjects reporting daily tobacco use at treatment entry will present with more severe gambling symptomology at treatment admission, have worse treatment completion outcomes, and gamble more days at a 6-month posttreatment follow-up.
Methods Sample
The sample consisted of 420 individuals who voluntarily sought treatment for PG at treatment facilities in Minnesota. The 11 treatment providers from which this sample was derived provided both group and individual therapy for PG. Eight treatment centers were outpatient provider organizations (i.e., more than one counselor at the program; n = 196), two were individual counselors providing outpatient treatment (n = 53), and one was an inpatient treatment center (n = 171). The Institutional Review Board of the University of Minnesota approved this study.
All eligible subjects met the criteria from the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, (DSM-IV;APA, 2000) for PG over the 6 months preceding treatment intake. At intake, all subjects completed a questionnaire inquiring about the use of all tobacco products (cigarettes, cigars, pipe tobacco, chewing tobacco, snuff) and the frequency of use over the past 12 months. Respondents were categorized as using tobacco daily, 3–6 times per week, less than 1–3 days per month, or no tobacco use over 12 months prior to treatment entry. Subjects with incomplete tobacco use results (n = 5) and those subjects who reported less than daily use (n = 30), often referred to in the literature as “chippers,” were excluded from analysis due to the poor understanding and classification of this population in the addiction literature (Shiffman, 1989; Presson, Chassin, & Sherman, 2002; Morissette et al., 2008) and wide distribution of use noted in our sample (n = 15 reported using tobacco <1–3 days per month and n = 11 reported tobacco use 3–6 days per week). This left a total final sample of n = 385 (n = 244 with daily tobacco use and n = 141 with no tobacco use).
Furthermore, in order to objectively assess differences that may bias results of the main outcome measures, a post hoc assessment of subjects lost to follow-up between treatment ending and the 6-month follow-up assessment (n = 110) was conducted and compared with those retained for all follow-up assessments on demographic and clinical variables.
Assessments
Admission and discharge assessments were administered by treatment staff while follow-up assessments were conducted by research staff via paper–pencil questionnaires. Participants were also asked to complete a survey 6 months following discharge from treatment. Research staff mailed out follow-up assessments and if they were not returned within 4 weeks, research staff called participants in order to complete the follow-up assessment over the telephone. Subjects were compensated with a gift card ($10) for completing and returning the surveys.
The two dependent variables were number of days gambled in the previous 4 weeks and treatment completion. The number of days gambled in the past 4 weeks was measured at 6 months following discharge. Treatment completion was determined by treatment staff as either fulfilling their requirements (i.e., completed a number of treatment sessions, participated in homework assignments, etc.) or a mutual agreement that treatment had come to an end.
Gambling Behavior and Psychopathology
Subjects were assessed using the Gambling Treatment Outcome Monitoring System (GAMTOMS; Stinchfield, Winters, Botzet, Jerstad, & Breyer, 2007) a valid and reliable, multidimensional screening tool utilized to assess current DSM–IV pathological gambling criteria and gambling treatment outcomes. Subjects were asked at what age gambling became problematic, if they had previous treatments, including inpatient or outpatient treatment for gambling, and responses were coded in a dichotomous manner. In addition, the number of GA sessions attended in the month prior to admission for treatment was reported. Financial consequences from gambling, including current gambling debt, were assessed at treatment intake. The South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987), a well-validated, 20-item questionnaire was used to assess clinical gambling severity criteria. PG severity and psychosocial dysfunction was also assessed using the Addiction Severity Index (ASI; McLellan, Luborsky, O’Brien, & Woody, 1980) modified for PG (Petry, 2007). Gambling frequency was assessed retrospectively for the past 4 weeks prior to baseline using the Gambling Timeline Followback (G-TLFB; Weinstock, Whelan, & Meyers, 2004).
Mental heath status at baseline was assessed using the Behavior and Symptom Identification Scale (BASIS-32; Eisen, Dill & Grob, 1994). The BASIS-32 measures difficulties during the course of treatment reported by the subject over the preceding 7 days using a 5-point Likert scale ranging from “no difficulty” to “extreme difficulty.”
Frequency of alcohol, marijuana, and other drug use not used for medicinal purposes, was assessed at intake via a self-report questionnaire. Subjects were also asked if they had undergone treatment during their lifetime for alcohol and drugs, other addictions (such as compulsive buying or sexual behavior), or other mental health problems.
Data Analysis
In order to identify variables that may be important for inclusion in the regression model, tobacco users and nonusers were compared on demographic and clinical variables assessed at baseline (or admission) using two-tailed t-tests for continuous variables and chi-square and Fischer’s exact testing for dichotomous variables. Those variables found to show significant differences between tobacco users and nonusers were then included in the regression. A multivariate analysis of variance with repeated measures was computed in order to examine response to gambling treatment. Linear regression was used to examine the relationship between tobacco use, treatment completion, treatment outcome, and various demographic and clinical variables at baseline. Three models were computed. The first examines the unadjusted relationship between daily tobacco use and number of days gambled. In the second model, we added demographics (age, gender, race, education, marital status) to the regression. In the final model, we added clinical characteristics measured at baseline. Logistic regression was used to examine the relationship between demographics and clinical characteristics measured at baseline and treatment completion (0 = no; 1 = yes) using Nagelkerke R2 (Nagelkerke, 1991). All tests of hypotheses were performed using a two-sided significance level of α = .05. All data were analyzed using IBM SPSS Statistics, Version 18 software.
ResultsAnalysis of the three separate groups included in the sample (individual outpatient, multicounselor outpatient, and inpatient) revealed that the groups were fairly homogenous. The inpatient group was more likely to have received treatment for gambling in the past (p < .001) and have less education (p = .032), but there were no other statistically significant differences between groups.
A total of 385 individuals voluntarily admitted to one of 11 inpatient or outpatient gambling treatment centers in Minnesota were included in this sample. Of these individuals, 244 (63.4%) reported daily use of tobacco products and 141 (36.6%) reported not having used tobacco products over the 12 months preceding treatment intake. Demographic and clinical characteristic comparisons of daily tobacco users versus nonusers are presented in Table 1. Daily tobacco users were significantly younger (p = .019) and had a lower overall level of education (p < .007) than nontobacco users. Daily tobacco users also had more frequent and severe PG symptoms at baseline, as compared with nontobacco users. They had an earlier age of PG onset (p = .003), met more DSM–IV PG criteria (p < .001), endorsed more addiction severity criteria as indicated by the SOGS (p = .004), had undergone more lifetime previous treatments for gambling (p = .043), and were more likely to have sought treatment for an alcohol or drug use disorder (p < .001). Daily tobacco users also scored higher on dysfunction for the ASI and BASIS-32 composite scores.
Baseline Demographic and Clinical Characteristics of 385 Pathological Gamblers Grouped by Tobacco Use
We also assessed changes in tobacco use from treatment intake to the 6-month follow-up to ascertain what percentage of our treatment sample either started using tobacco or quit using tobacco over that time period. We found that only 4.1% of nontobacco users began using daily and 2.9% of users quit.
Effectiveness of Treatment Over Time
An analysis of the effectiveness of treatment over time revealed that all variables showed improvement over time but there were no significant interaction effects for tobacco use by time (see Table 2). The number of days gambled over the past preceding 28 days, as well as the number of financial concerns, PG criteria met on the DSM–IV, and number of SOGS items endorsed all significantly improved over time.
Comparison of Intake, Discharge, and 6-Month Follow-Up Assessment by Pretreatment Tobacco Use Status
Treatment Completion
The relationship between tobacco use and treatment completion was examined using logistic regression (see Table 3). Of the 385 subjects who entered treatment, 268 (69.6%) completed treatment. A total of 168 (69.1%) of the daily tobacco using and 100 (71.9%) nontobacco using cohort completed treatment, a nonsignificant between-groups difference (p = .564). Logistic regression of gambling treatment completion while controlling for significant demographic and clinical variables revealed few significant effects. Tobacco use was not related to treatment completion and the relative risk of daily tobacco use in relation to treatment completion was 1.14 (95% confidence interval [CI] = 0.72–1.81), accounting for less than 1% of the variance (R2 = .01). Model II and Model III explain 8% and 19% of the variation, respectively. The ASI composite score had the highest odds ratio (OR = 5.57; 95% CI: 1.02–30.40) and was statistically significant along with age (OR = 0.97; 95% CI: 0.94–0.99; p = .031) and being married (OR = 1.75; 95% CI: 1.01–3.02; p = .045). Composite scores on the BASIS-32 garnered the most significant association with treatment completion with higher scores on the BASIS-32 indicating significantly lower rates of treatment completion (OR = 0.47; 95% CI: 0.28–0.77; p = .003).
Logistic Regression Results for the Relationship Between Selected Demographic, Baseline Clinical Variables and Treatment Completion (n = 268)
Days Gambled at 6-Month Follow-Up
At 6-month follow-up, subjects reported gambling an average of 1.9 ± 4.0 days [range 0–28 days] over the 4 weeks preceding the follow-up assessment and a total of 157 (40.8%) reported complete abstinence from gambling. As such, the distribution for this sample is left-skewed. Daily tobacco users and nonusers did not, however, differ significantly in regard to number of days gambled (p = .306) at the 6-month follow-up.
Linear regression was used to assess the relative strength of the relationships between the independent variables and number of days gambled at 6 months. As shown in Model I on Table 4, tobacco use was not significantly associated with the number of days gambled at 6 months. In Model II, race was the only significant demographic variable that was significant. Respondents who reported that they were from a minority cultural group had a greater number of days gambled than did white subjects (B = 1.68; 95% CI = 0.04–3.32). None of the clinical variables at baseline, added at Model III, were associated with number of days gambled at 6 months. Moreover, only 8% of the variance in days gambled at the 6-month follow-up could be accounted for by these variables.
Linear Regression Results for the Relationship Between Selected Demographic, Baseline Clinical Variables, and Number of Days Gambled at 6-Month Follow-Up (n = 275)
Lost to Follow-Up
Inclusive of drop-outs, a total of 275 subjects reached their 6-month follow-up anniversary and completed a 6-month follow-up questionnaire, yielding a follow-up response rate of 71.4%. Subjects lost to follow-up were compared with those who completed the 6-month follow-up assessment (see Table 5). Subjects lost to follow-up were significantly younger (p = .012), as compared with those who completed this assessment. There were no other sociodemographic or clinical characteristics that predicted being lost to follow-up.
Lost to Follow-Up: Demographic and Clinical Characteristic Evaluation
DiscussionThis study sought to examine the impact of daily tobacco use as potential markers for gambling treatment success and completion. Consistent with our hypotheses and previous research (McGrath & Barratt, 2009), daily tobacco using gamblers in treatment presented with more severe symptoms at treatment intake. They had an earlier age of problem gambling onset, had more mental health problems as assessed with the BASIS-32, and were more likely to have had previous treatments for PG.
Daily tobacco use did not, however, significantly affect treatment completion or the number of days gambled at the 6-month posttreatment follow-up as we had hypothesized. One explanation could be that perhaps the gambling interventions used at the treatment centers in this analysis had a positive global effect on the impulsive nature of these individuals and thereby reduced gambling in concert with reductions in tobacco use. Follow-up assessment of tobacco use status changes, however, revealed that only 3% of nontobacco users started using and 4% of tobacco users stopped using, a nonsignificant between-groups difference. Therefore, within 6 months of discharge, there was no significant effect on tobacco use. Another explanation could be that although tobacco use contributes to worsening gambling symptoms, possibly through a complex biological, environmental, and genetic etiology (Grant & Potenza, 2005; Grant, Black, Stein, & Potenza, 2009), it does not interfere with the therapeutic effects of treatment on gambling. Finally, another possible explanation for any substantive differences between tobacco and nontobacco using gamblers is the substantial heterogeneity in tobacco-using gamblers. That is, some nicotine using gamblers may have their abstinence jeopardized by continuing to use tobacco whereas others will not. Although this study failed to illustrate a substantive effect of tobacco use on gambling treatment, previous research illustrates the deleterious effects of nicotine use on substance abuse treatment (Frosch, Shoptaw, Nahom, & Jarvik, 2000). In other studies, however, nicotine has been shown to enhance cognitive functioning in some individuals (Heishman, Kleykamp, & Singleton, 2010; Mooney et al., 2011) or have minimal negative impact on cognitive performance (Businelle, Apperson, Kendzor, Terlecki, & Copeland, 2008). Many gamblers who smoke indicate that smoking has a calming influence on their urges to gamble whereas others may find it urge-inducing. Because we did not assess urges to gamble in this sample, this would be a useful assessment for future research on this topic. Furthermore, neurocognitive assessment of nicotine dependent pathological gamblers would be helpful in discerning the biological differences between tobacco-using and nonusing gamblers.
Research on alcohol users in treatment has illustrated that those encouraged to quit smoking are more likely to maintain abstinence (Bobo et al., 1998). Despite the disproportionately high rate of tobacco use among pathological gamblers, research, to date, has yet to address the impact of continued tobacco use on gambling treatment outcome. In the realm of substance addiction research, Prochaska, Delucchi, and Hall (2004) performed a meta-analysis of 19 randomized controlled clinical trials of addiction treatment centers to examine the impact of smoking cessation on alcohol or drug abstinence. They found that those who underwent smoking cessation training concomitant with their addiction counseling were 25% more likely to maintain long-term drug and alcohol sobriety (Prochaska et al., 2004). Because smoking cessation was not captured in this sample of individuals, it may underscore the importance of incorporating such programs for those in gambling treatment and examining this variable in future treatment studies.
Limitations
Several limitations must be noted in this study. First, a number of subjects (28.6%) were lost to follow-up prior to the 6-month assessment and consequently, the high rate of abstinence observed may not be entirely accurate. We therefore assessed for demographic or clinical differences between treatment completers and those lost to follow-up (see Table 4) and found only age differences between groups. Since demographic and clinical variables were controlled for in the analysis, however, and no other significant differences were noted in Table 4, the rate of abstinence observed may be accurate. Second, the sample consisted of people who volunteered to participate in the study and they may be different from those who refused as well as nontreatment seeking gamblers in the general population. Third, a formal assessment of urges to gamble was not completed by the treatment centers. In addition to examining the time spent engaging in the actual addictive behavior, research has illustrated the importance of addressing a client’s urges to engage in addictive behavior (Pallanti, DeCaria, Grant, Urpe, & Hollander, 2005; Kim et al., 2001) as a means of predicting treatment response (Grant, Kim, Hollander, & Potenza, 2008). This study did not address clients’ urges or thoughts to gamble nor did it control for the use of psychotropic medication which may have been administered concomitantly with psychotherapy provided by each of the treatment centers. Future research should assess these variables and their subsequent effect on treatment completion and gambling severity. Finally, the study relied on client self-report and consequently the data may be biased by weaknesses of self-report, including inaccurate recall and intentional deception. The assessments utilized for this study, however, have demonstrated satisfactory evidence of reliability, validity, and classification accuracy, which helps to mitigate this limitation.
ConclusionsOur findings suggest that daily tobacco use does not negatively affect treatment completion or long-term outcome for treatment-seeking pathological gamblers. The prevalence of tobacco use in this cohort of pathological gamblers (63.4%) was consistent with rates noted in previous samples of pathological gamblers and significantly higher overall, as compared with rates of use among the general public of the United States (12.8%) and Minnesota (21%; Minnesota Department of Health, 2011). Given the deleterious effects of tobacco on physical and mental health, tobacco use should be addressed concurrently with gambling treatment as a means of improving the overall health of the individual.
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Submitted: December 14, 2011 Revised: July 13, 2012 Accepted: July 23, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (3), Sep, 2013 pp. 696-704)
Accession Number: 2012-23735-001
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Record: 70- The role of goals and alcohol behavior during the transition out of college. Radomski, Sharon A.; Read, Jennifer P.; Bowker, Julie C.; Psychology of Addictive Behaviors, Vol 29(1), Mar, 2015 pp. 142-153. Publisher: American Psychological Association; [Journal Article] Abstract: Personal goals are desired outcomes that guide behavior (Palfai, Ralston, & Wright, 2011), and are typically oriented around age-appropriate developmental tasks (e.g., college graduation, employment). Goals and their pursuit take on much salience during senior year of college as individuals prepare for the transition into adult roles. This also is a time during which naturalistic changes in alcohol consumption are occurring. These changes may impact the relationship between age-related goals and their attainment, thus compromising the likelihood of a successful transition out of college. The present study examined whether and how changes in drinking over senior year moderate the association between achievement goals and related developmental task attainment as students move toward transitioning out of college. Alcohol-involved college seniors (N = 437; 62.5% female) were assessed via web survey in September of their senior year and again 1 year later (T4). Results of multinomial logistic regression revealed that greater achievement goals were predictive of college graduation (vs. remaining a continuing undergraduate), but only for those whose drinking decreased during senior year. Among those graduated by T4 (n = 307), achievement goals predicted pursuing graduate education (vs. being unemployed), but only for students whose drinking increased during senior year. Thus, achievement goals are important predictors of goal attainment as students prepare to transition out of college, and these goals can interact with drinking in complex ways during this time. Findings suggest that interventions aimed at bolstering personal goals and reducing drinking during senior year may increase the likelihood of successful transitions out of the college environment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Role of Goals and Alcohol Behavior During the Transition out of College
By: Sharon A. Radomski
Department of Psychology, The State University of New York at Buffalo;
Jennifer P. Read
Department of Psychology, The State University of New York at Buffalo
Julie C. Bowker
Department of Psychology, The State University of New York at Buffalo
Acknowledgement: This work was supported by a grant from the National Institute on Drug Abuse (R01DA018993) to Jennifer P. Read. We thank Drs. Jennifer E. Merrill, Sherry Farrow, Craig Colder, and Jeffrey Wardell, as well as Ashlyn Swartout, Jackie White, and the members of the UB Alcohol Research Lab for their many efforts to support data collection for this study.
According to Havighurst (1948), each phase of the lifespan consists of specific developmental tasks that must be completed before moving onto the next phase. Emerging adulthood (ages 18–25) is a distinct developmental period in the life span that has been identified as a critical turning point, and is associated with its own unique developmental tasks (Schulenberg, Bryant, & O’Malley, 2004; Tanner, 2006). Occurring at the juncture between adolescence and adulthood (Arnett, 2000), it is during the emerging adult years that individuals begin the process of coming into their own as independent adults. Developmental tasks of this period are those relevant to this transition, often rooted in domains such as individual identity formation and professional development. Failure to successfully attain these developmental tasks is associated with a host of deleterious outcomes, including problem behaviors (e.g., heavy alcohol use), emotional distress, and psychopathology (Aseltine & Gore, 2005; Bell & Lee, 2008; Havighurst, 1948; Nurmi & Salmela-Aro, 2002; Schulenberg, Sameroff, & Cicchetti, et al., 2004; Shulman, Kalnitzki, & Shahar, 2009), and is costly to society (Schneider & Yin, 2011). Educational and employment attainment are two particularly important developmental tasks for emerging adults in college, and successful mastery of these tasks is critical to a successful transition into independent adulthood, as each of these tasks is associated with significant economic and other longer-term benefits (Carnevale, Jayasundera, & Cheah, 2012; Wanberg, Zhu, & Van Hooft, 2010; Zaback, Carlson, & Crellin, 2012). As such, the identification of factors that either aid or impede progress toward these tasks is an important area of focus on both individual and societal levels.
Personal Goals and Developmental Task AttainmentA goal is the identification of and commitment to an end result that serves as a source of motivation for directed effort toward that objective. Goals are specific to each individual, yet generally coincide with the tasks of the developmental period (Nurmi, 1992; Palfai & Weafer, 2006; Salmela-Aro, Aunola, & Nurmi, 2007). For example, goals related to education, occupation, and relationships are most commonly reported by emerging adults (Nurmi, 1992; Palfai & Weafer, 2006). Motivational theories, theories that incorporate components of inner drive and their influence on behavior, argue that it is not only timing, but also the specific focus of the goals that gives them potency. That is, the more closely linked or specific a goal is to a target behavior, the greater the likelihood for goal attainment. This assertion is supported by data suggesting that goals in a particular domain predict task advancement in that same domain (Nurmi, Salmela-Aro, & Koivisto, 2002; Salmela-Aro et al., 2007). Importantly, evidence suggests that personal goals assume greater or lesser relevance at different times, especially during times leading up to transitional periods (Roberts, O’Donnell, & Robins, 2004; Salmela-Aro et al., 2007). As such, the senior year of college may be a time when developmental tasks and goals for attaining them may take on particular salience, as students prepare for the transition out of the college environment and into adult roles (Arnett, 2000; Murphy, Blustein, Bohlig, & Platt, 2010; Roisman, Masten, Coatsworth, & Tellegen, 2004).
It has been noted that the association between goals and their attainment is complex, and that individual difference variables may contextualize this association (Kanfer, 1987). Though the list of such contextual factors is myriad (e.g., Bowen, Chingos, & McPherson, 2009; Carnevale et al., 2012; Zimmer-Gembeck & Petherick, 2006), one in particular stands out as being of potential importance, alcohol consumption.
Alcohol, Goals, and Developmental Task AttainmentHigh rates of heavy alcohol use among college students have been well documented, and such patterns of use are likely to interfere with goal attainment (Hingson, Heeren, Winter, & Wechsler, 2005; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994). Research on emerging adults suggests that alcohol may play a role in goal attainment (Palfai & Weafer, 2006; Rhoades & Maggs, 2006; Wright & Palfai, 2012). Data show that alcohol, as well as other drug use, has a negative impact on academic performance in college (Pascarella et al., 2007) as well as on the likelihood of postgraduation unemployment (Arria et al., 2013). These studies demonstrate the lasting negative impact that college substance use can have on attainment of key developmental tasks.
Alcohol use peaks at ages 21 and 22, and then there is a subsequent decline in—or “maturing out” of—alcohol consumption (Chen & Kandel, 1995; Patrick & Schulenberg, 2011; Sher, Bartholow, & Nanda, 2001). This pattern is more marked in college students compared to their noncollege peers (White, Labouvie, & Papadaratsakis, 2005). Though the precise causes appear to be complex, this naturalistic maturing out of heavy drinking is generally believed to occur in the service of transitioning into more adult roles (Bachman et al., 2002; Bachman, Wadsworth, O’Malley, Johnston, & Schulenberg, 1997; Leonard & Rothbard, 1999; O’Malley, 2004–2005; Parra, Krull, Sher, & Jackson, 2007; Staff et al., 2010).
Indeed, alcohol use throughout the college years is dynamic and influenced by a variety of factors (Del Boca, Darkes, Greenbaum, & Goldman, 2004). Importantly, periods of transition in particular are associated with changes in alcohol use as individuals prepare for and adapt to evolving life circumstances (Jackson & Schulenberg, 2013; Sher & Gotham, 1999; White et al., 2006). These naturalistic drinking changes may have significant implications for developmental task attainment. Though many college students mature out of heavy drinking once they leave the campus environment, others do not, and thus are at risk for poor adaptation to adulthood, including problem drinking and associated negative health outcomes (Bennett, McCrady, Johnson, & Pandina, 1999; Chen & Kandel, 1995; Gotham, Sher, & Wood, 1997; Jackson, Sher, Gotham, & Wood, 2001; Sher & Gotham, 1999).
Historically, role transitions (e.g., employment, marriage, parenthood, etc.) have been believed to be the primary influence on the maturing out of heavy drinking (Yamaguchi & Kandel, 1985). Yet, emerging evidence has begun to suggest that the part that these role transitions play in the evolution of alcohol involvement is quite complex, resulting from a dynamic and interactive transaction between the individual and his or her environment (Littlefield, Sher, & Wood, 2010; Schulenberg, Sameroff, & Cicchetti, 2004; Vergés et al., 2012). For example, the transition into a new role may influence alcohol use as the individual may alter his or her typical drinking patterns in preparation for, or in response to, a new role. Additionally, changes in drinking patterns may influence transitions in that drinking behavior may be incompatible with a new role, or one may select their new role based on changes in drinking. Though research has examined changes in drinking at the transition into college and how such changes may interact with college functioning and development (e.g., Cleveland, Lanza, Ray, Turrisi, & Mallett, 2012; White et al., 2006), there has been minimal examination of other important transitions in young adulthood, such as the transition out of college. This is an important oversight, as these naturally occurring changes in drinking could alter the extent to which emerging adults are able to attain their developmental task goals as they transition into mature adulthood. For example, reductions in alcohol use over senior year may strengthen the relationship between goals and developmental task attainment while increases in alcohol use may weaken this association. Enhanced understanding of the factors that influence the transition out of college will shed light on potential target variables for further research and potentially inform opportunities for intervention.
In summary, emerging adults face a number of age-appropriate developmental tasks, primarily among which are educational and employment attainment. Despite the fact that task-related goals are associated with attainment of these tasks, goal theory suggests that there are individual factors (e.g., changes in drinking behavior) that also may qualify this association. Periods of transitions are a time of naturalistic change in drinking (Jackson & Schulenberg, 2013; Sher & Gotham, 1999; White et al., 2006), and as such, these changes are one factor that may exert such a qualifying influence, moderating the association between personal goals and developmental task attainment. Importantly, if changes in alcohol use prove to be relevant to goal attainment at this critical point of transition, senior year interventions aimed at promoting successful transitioning out of college and into the workforce should incorporate this information appropriately. Accordingly, the examination of this potential moderating effect was the aim of the present study.
The Present StudyThe present study examined changes in alcohol use over senior year as a moderator of the association between personal goals and the attainment of age-appropriate developmental tasks of emerging adulthood during the transition out of college (see Figure 1). We focused on two specific developmental tasks relevant to the transition out of the college environment, graduation from college at the end of senior year, and obtaining a postgraduation occupation. We expected that task-specific goals (i.e., achievement goals) would uniquely predict attainment of these two developmental tasks. As alcohol use has been shown to be associated with unique developmental characteristics of emerging adults pursing adult role transitions (Arnett, 2005; Palfai, 2006), we also sought to examine the moderating role of changes in alcohol use in attaining these developmental tasks.
Figure 1. Conceptual model of current study. Underlining signifies the predictor of interest. Italics signify reference groups.
HypothesesTwo primary a priori hypotheses pertaining to two developmental tasks (i.e., educational attainment and obtainment of postgraduation occupation) were postulated:
Hypothesis 1: Achievement goals in September of fourth year in college (T1) would predict (a) education attainment and (b) postgraduation occupation (work or educational) status measured the following September (T4), controlling for gender, posttraumatic stress disorder (PTSD) status, and other age-related goals.
Hypothesis 2: Changes in quantity of alcohol consumption leading up to the transition out of college would moderate this relationship. Secondary hypotheses related to moderation effects are as follows: (Hypothesis 2a) Decreased drinking over senior year would strengthen the positive effect of achievement goals on the likelihood of graduating from college at the end of senior year and being employed in a full-time paid position or in graduate school following undergraduate college graduation. (Hypothesis 2b) Increased drinking over senior year would weaken the positive effect of achievement goals on the likelihood of graduating from college at the end of senior year and being employed in a full-time paid position or in graduate school following undergraduate college graduation.
Method Procedure
All procedures were approved by the relevant university institutional review boards. Participants for the present study were drawn from a larger study investigating trauma and substance use. Details of recruitment procedures have been published previously (Read, Ouimette, White, Colder, & Farrow, 2011; Read et al., 2012), but will be briefly reviewed here. Matriculating students (ages 18–24) at two midsized public U.S. universities (one in the northeast and one in the southeast) were recruited in the summer prior to matriculation into college to participate in a longitudinal, Web-based study. A baseline screen was administered to determine eligibility for the prospective portion of the study. The response rate for this initial screening was 58%, consistent with other studies using similar methods (Larimer, Turner, Mallett, & Geisner, 2004; Neighbors, Geisner, & Lee, 2008). From this sample, those meeting traumatic stress criteria (i.e., at least one traumatic event and three or more symptoms of PTSD) and an equal number of randomly selected control participants (i.e., those who were below this threshold; total n = 692) were invited to participate in the longitudinal study. Of these, a total of 560 (81% response rate) completed a baseline survey in September of their first year of college. Data from three participants were excluded from analyses because evidence indicated haphazard responding to the survey items. Thus, the final longitudinal sample consisted of 557 participants. Data were collected in two cohorts 1 year apart beginning in 2006. Participants were assessed via web survey six times in the first year of college and four times each year (typically September, December, February, and April) for the remainder of the study. The first cohort completed 6 years of data collection, and the second cohort completed 5 years. Participants were compensated with a $35 gift card for each survey completion.
Participants: The Present Sample
The present study focused on the period during which students were preparing to transition out of college (see Figure 1). Accordingly, our first time point (T1) was the September survey in the fourth postmatriculation year. The last time point of the present study is the following year’s September survey in the fifth postmatriculation year (T4). Only those participants who reported being college seniors and having consumed alcohol at least once during the previous 30 days at the T1 time point were included in the present study (N = 437, 62.5% female). Participants were also surveyed in February (T2) and April (T3) of their senior year. A subset of this sample (n = 307) was used for the postgraduation occupation outcome variable analyses, for which only those who had reported being graduated at T4 were included (see Table 1 for sample and subsample demographic information). As the original sample was recruited for a strong representation of students with PTSD symptoms, 13.7% (n = 60) of the current sample reported symptoms that were consistent with a PTSD diagnosis at college matriculation. Thus, PTSD status at matriculation into college was modeled in the current analyses to control for the possible influence of PTSD symptoms on associations of interest.
Demographics of Sample and Subsample
Measures
Demographics
Participants reported on several demographic characteristics including gender, age, and ethnicity.
Alcohol use
Participants completed a measure of typical daily alcohol use based on the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985). At every time point, participants reported the average number of standard drinks consumed on a typical Monday, Tuesday, Wednesday, and so forth in the past 30 days. Respondents were given standard drink conversion charts including a definition of a standard drink to enhance reporting accuracy. Summing the number of drinks across all 7 days provided a measure of typical weekly alcohol use over the past 30 days at each time point.
Goals
The measure of goals used for the present study is based on Palfai and Weafer’s (2006) work on goal constructs in emerging adulthood. Using Little’s (1983) personal projects analysis methodology, Palfai and Weafer identified four domains of goals (achievement, fitness/recreational, interpersonal, and intrapersonal) and two goal engagement constructs (goal meaning and goal efficacy). According to Palfai and Weafer (2006), goal meaning was interpreted as reflecting goal commitment and importance, and goal efficacy was interpreted as signifying goal “difficulty, likelihood of success, perceived progress, feel in control of project” (Palfai & Weafer, 2006, p.132).
For the present study, we relabeled the goal meaning and goal efficacy constructs as “goal commitment” and “goal progress,” to reflect two distinct dimensions of goal pursuit. We then asked participants about their goals in the four domains at T1. Thus, this measure included one question about goal commitment and one question about goal progress for each of the four goal domains: achievement (school, work), fitness/recreation, interpersonal (relationships with other people including romantic relationships), and intrapersonal (within yourself, e.g., emotions). For each goal domain, participants were asked how committed they were to their own goals in that domain and how much progress they had made toward their goals in that domain. For example, “How committed are you to your own goals in achievement?” and “How much progress have you made toward your own goals in achievement?” Goal Commitment response options ranged from 0 to 3 (0 = I don’t have any goals in this area, 1 = not committed, 2 = somewhat committed, and 3 = very committed). Goal Progress response options ranged from 1 to 4 (1 = no progress, 2 = very little progress, 3 = some progress, and 4 = a lot of progress).
Developmental tasks
Participants were asked to indicate their level of education attainment and postgraduation occupation status at T4. The choices for education attainment included college freshman, sophomore, junior, senior, not currently enrolled in college, graduated, fifth-year senior, sixth- or-later-year senior, and graduate student. Postgraduation occupation status was assessed with the item: “What is your current employment status?” Response options included employed in a full-time paid position, employed in a part-time paid position, employed in an unpaid position, have accepted a position but have not yet started this job, and unemployed.
Data Analytic Approach
In order to capture the transitional period for the current study, we examined the timeframe spanning from September of participants’ senior year in college (T1) to the following September (T4). This prospective design allowed for the investigation of the interaction of personal goals and changes in alcohol use in predicting a successful transition out of the college environment. See Figure 1 for time point information.
Alcohol variables
Data from the DDQ (Collins et al., 1985) in September (T1), February (T2), and April (T3) were used to assess relative changes in typical weekly alcohol use from fall to spring. Given that there was no mean change in drinking between T2 and T3, and because drinking at each of these time points was highly correlated (r = .87), T2 and T3 typical weekly drinking over the past 30 days was averaged across time points to create a more reliable measure of spring drinking. For participants missing drinking data either at T2 or T3, we used the available value for either T2 or T3 as the average spring drinking variable. This minimized the impact of missing data.
We were interested in how naturalistic changes in drinking during senior year might alter (moderate) the influence of goals on task attainment. To capture this change, we created a variable representing relative changes in typical weekly drinking from fall to spring, by regressing drinking at T3 (averaged with T2) on drinking at T1. We used the residuals as the change score variable. This approach was chosen over other approaches (e.g., controlling for T1 drinking, using a difference score) because it is base-free and, as such, provides a meaningful metric by which an individual’s behavior change may be understood relative to others’ in the group that is not confounded by drinking at baseline. During a transition such as this, when alcohol use is declining at the mean level, residual change scores allow us to examine increases or decreases in drinking relative to the group as a moderator of the effects of personal goals on developmental task attainment. As such, this measure of change was well suited to addressing our study’s objectives. This process was repeated for the creation of the change in alcohol use variable for the subsample of college graduates.
Goal variables
Goal attainment was conceptualized as a function of making a commitment to goals in a given area and then making progress toward those goals. This conceptualization was supported by the high correlation of goal commitment and goal progress within each goal domain in our study (ranging from .41 to .67). Accordingly, within-domain scores for these two (commitment, progress) related variables were multiplied and the product was used as an index of domain specific goal directedness.
Developmental task outcome variables
Developmental task outcome variables were categorized to portray attainment status for each developmental task, similar to procedures by Schulenberg, Bryant, et al. (2004). Education attainment was categorized to reflect graduation status in the following way: (Group 1; n = 172) graduated, no further education, (Group 2; n = 136) graduated, graduate student, (Group 3; n = 116) continuing undergraduate, (Group 4; n = 13) not enrolled/dropped out. Analyses for postgraduation occupation only included those participants who had successfully graduated from college (n = 307). The outcome variable postgraduation occupation had a range of response options: unemployed (n = 25), employed part-time paid (n = 47), accepted a job but not yet started (n = 4), employed full-time paid (n = 92), graduate student (n = 136), employed unpaid (n = 3). These response options were organized into three groups: (Group 1; n = 96) represented attainment the developmental task (i.e., full-time, paid employment or acceptance of employment offer but not yet started) and henceforth is referred to as employed, (Group 2; n = 75) represented stalled attainment in this developmental task as we operationalized it (i.e., those who were unemployed, working part-time, or working but unpaid) and henceforth is referred to as unemployed, and (Group 3; n = 136) those who reported being in graduate school. Those who reported being in graduate school were classified in a separate group due to the range of possible positions (e.g., internship, volunteer, part-time employed) that a graduate student may fulfill. The purpose of this categorization was to capture the commonalities of being a graduate student following college graduation in terms of the interaction of personal goals and changes in drinking over senior year.
Data management
Participants who stopped participating in the study or were missing data at relevant time points were removed (n = 26, 5.62%). Model variables (alcohol use and goals) were evaluated for skew, kurtosis, and outliers. Outliers were addressed by adjusting outlier scores consistent with guidelines from Tabachnick and Fidell (2000). All variables were centered in moderation analyses.
Analyses
Multinomial logistic regression was used to predict education attainment based on T1 (September, senior year) achievement goals, while controlling for the influence of other, related developmentally appropriate types of personal goals (i.e., interpersonal, intrapersonal, fitness/recreation). As gender differences in goals have been observed (see Nurmi, 1992; Rhoades & Maggs, 2006; Salmela-Aro et al., 2007), we controlled for gender in our analysis. PTSD status at college matriculation also was controlled.
We examined the moderating influence of during-year changes in drinking on this association (Achievement Goal × Change in Alcohol Use), using a residual alcohol use score to capture change in drinking in that last year. In this analysis, we were interested in comparing those who met the developmental task of graduating from college versus those who did not. Thus, continuing undergraduates served as the reference group.
Multinomial logistic regression also was used to predict postgraduation occupation status based on T1 (September, senior year) achievement goals, controlling for other types of personal goals, gender, and PTSD status. Those considered to have stalled in attaining the developmental task of full-time paid employment served as the reference group (unemployed) in these analyses.
Results Descriptive Statistics
Scores for each of the four domains of goal directedness (achievement, fitness/recreation, interpersonal, and intrapersonal) ranged from 0 to 12. Across these domains, goals related to achievement were rated most highly in this sample (M = 9.38, SD = 2.62). Typical weekly drinking, as measured by the DDQ, varied over senior year but reflected a decrease in alcohol use from fall to spring semester at the mean level. The residual change in typical weekly drinking from fall to spring, indicative of changes in drinking relative to the group, had a standard deviation of 5.83. Table 2 contains descriptive information for the total sample and the subsample of college graduates.
Descriptive Statistics for Sample and Subsample
Education Attainment: Goals Predicting Education Attainment 1 Year Later and Test of Changes in Alcohol Use Moderation Effects
Results revealed that higher levels of T1 achievement goals were associated with an increase in the odds of being graduated (B = .10, SE = .05, p = .03, OR = 1.11) and a graduate student (B = .19, SE = .05, p < .01, OR = 1.21), relative to being a continuing undergraduate. Additionally, higher levels of T1 interpersonal goals were associated with a decrease in the odds of being a graduate student (B = −.08, SE = .04, p = .04, OR = .92), relative to being a continuing undergraduate. No other goals were significant predictors of education attainment and the effects of goals on education attainment remained the same when drinking variables were included for moderation analyses. A test of these moderation effects revealed a significant Achievement Goal × Change in Alcohol Use interaction predicting the odds of being graduated versus a continuing undergraduate (B = −.02, SE = .01, p < .05, OR = .98). Although gender significantly predicted education attainment, PTSD status did not. Controlling for these two variables did not affect the moderation analysis results (see Table 4 for the effects of goals on education attainment and the omnibus test results).
The Effect of Goals on Education Attainment and Omnibus Moderation Analysis
In probing the significant interaction, we recentered the distribution of the alcohol use variable at one standard deviation above and below the mean to represent increased and decreased drinking over senior year, respectively (Aiken & West, 1991). As the standard deviation for the variables that represented change in alcohol use from fall to spring was 5.83, when probing moderation analyses, the regression coefficient for achievement goals represented the effect of achievement goals on education attainment for those who decreased or increased drinking, by 5.83 drinks on a typical week over senior year. Simple slopes analyses revealed that achievement goals did not predict being Graduated versus being a continuing undergraduate for those whose drinking increased over the spring semester of their senior year (B = −.02, SE = .08, p = .79, OR = .98). Yet, for those whose drinking decreased over this period, achievement goals were predictive of being graduated (B = .22, SE = .08, p < .01, OR = 1.25). See Figure 2 for a plot of the simple slopes.
Figure 2. Relationship between achievement goals and probability of graduating versus being a continuing undergraduate moderated by changes in alcohol use over senior year. The line representing decreased drinking is significant and the line representing increased drinking is not.
Postgraduation Occupation Status: Goals Predicting Postgraduation Occupation Status 1 Year Later and Test of Changes in Alcohol Use Moderation Effects
Results revealed that higher levels of T1 achievement goals were associated with an increase in the odds of being a graduate student (B = .13, SE = .06, p = .03, OR = 1.14), relative to being unemployed. No other goals significantly predicted postgraduation occupation and the effects of goals on postgraduation occupation remained the same when drinking variables were included for moderation analyses. The omnibus moderation test revealed a significant Achievement Goal × Change in Alcohol Use interaction for those who were in graduate school versus those who were unemployed (B = .04, SE = .01, p < .01, OR = 1.04). However, the hypothesized interaction for the employed versus unemployed contrast was not significant (B = .01, SE = .01, p = .65, OR = 1.01). Neither gender nor PTSD was a significant predictor of postgraduation occupation status, and controlling these variables did not affect the moderation analysis results (see Table 5 for omnibus test results).
The Effect of Goals on Postgraduation Occupation and Omnibus Moderation Analysis
In probes of the significant interaction, the regression coefficient for achievement goals represented the effect of achievement goals on postgraduation occupation status for those who decreased or increased drinking their typical weekly drinking from fall to spring by 5.77 (the standard deviation for the alcohol change variable) drinks. Simple slope analyses indicated that for those whose drinking increased from fall to spring, a one unit increase in achievement goals increased the odds of being in graduate school versus unemployed (B = .36, SE = .12, p < .01, OR = 1.44). That is, higher levels of achievement goals were associated with a higher likelihood of being in graduate school versus unemployed for those whose drinking increased over senior year. Level of achievement goals was not predictive of being in graduate school versus unemployed for those whose drinking decreased over senior year (B = −.07, SE = .09, p = .49, OR = .94). See Figure 3 for a plot of these simple slopes.
Figure 3. Relationship between achievement goals and probability of being in graduate school versus unemployed moderated by changes in alcohol use over senior year of college. The line representing increased drinking is significant and the line representing decreased drinking is not.
DiscussionTo our knowledge, this is the first examination of personal goals and developmental task attainment in the transition out of college, and the role that changes in alcohol use may play in this association. Findings shed light on these processes, and highlight several directions for future inquiry. These are discussed next.
Consistent with findings from Nurmi (1992) and Palfai and Weafer (2006), students in our sample reported the highest level of goal directedness for achievement goals. This supports the notion that goals are oriented around developmentally appropriate tasks. As hypothesized, higher levels of achievement goals were associated with a greater likelihood of being graduated or a graduate student versus a continuing undergraduate and changes in alcohol use moderated the association between achievement goals and the probability of attaining age-appropriate developmental tasks (i.e., education attainment and postgraduation occupation). On average, alcohol use decreased over the course of senior year. Yet there was individual variability, and we observed some interesting findings regarding the role that such changes in drinking during this time play in the association between goals and task attainment.
Education Attainment
Our findings revealed that naturally occurring changes in drinking during senior year moderated the relationship between achievement goals and graduation status. Those with higher levels of achievement goals at the beginning of their senior year were more likely to be graduated from college 1 year later than they were to be continuing on for a fifth undergraduate year if their drinking decreased over senior year. In contrast, the association between goals and attainment was not significantly different from zero for those whose drinking increased during the senior year. This finding suggests that increased drinking interfered with the relationship between achievement goals and education attainment in a way that renders these goals irrelevant, at least with respect to this academic outcome. This illustrates the importance of achievement goals in senior year and the potential negative impact of increases in drinking over senior year on education attainment.
In considering this interaction, it is worth taking note of the left side of this graph (see Figure 2) which indicates that those who had low goals and increased their drinking showed a greater likelihood of being graduated than those with low goals whose drinking decreased. As the literature suggests, achievement-related goals are the most reported goals for emerging adults and thus the majority (approximately 75%) of the current sample reported high levels of achievement goal directedness. As such, there was not much representation of those at the lower end of the achievement goal spectrum. Accordingly, we speculate that the most important conclusion to be drawn from this interaction is that changes in drinking during the senior year really matter for those participants who start that year with high levels of achievement goals. Although modest, this effect is meaningful given the large proportion of students who endorsed high achievement-related goals. Despite their high goals for achievement, students whose drinking increased were slower to reach an important developmental milestone, college graduation. Moreover, given the innumerable variables that may influence graduation rates, a 10% increase in the predicted probability of graduating for such individuals whose drinking decreases relative to others’ over senior year is a notable effect. At present, however, this conclusion remains speculative. Replication of this finding in future work will build confidence in this interpretation.
We did not find evidence of moderation by alcohol changes for the likelihood of being a graduate student versus a continuing undergraduate. This is intriguing, as beginning graduate study might be considered by many to reflect a transition to a more adult role. Thus, one might expect changes in alcohol consumption to play a similar role for those who become graduate students as they do for those who graduate college and go on to other occupational endeavors (the graduated group in the present study). Instead, we observed more similarity between graduate students who are furthering their education right out of college and continuing undergraduates, at least with respect to the interplay between goals and alcohol involvement. This may speak to the potent influence of the campus environment when it comes to drinking, as neither graduate students nor continuing undergraduates actually leave the college environment.
Postgraduation Occupation Status
Contrary to our hypotheses, the relationship between achievement goals and attaining employment was not moderated by changes in drinking over senior year. Additionally, consistent with findings from Salmela-Aro et al. (2007), achievement goals were not predictive of being employed at the follow-up assessment. Taken together, these null findings suggest that perhaps there are other factors that influence postgraduation occupation status that may be more important than goal directedness. For example, limited job availability may interfere with attaining employment more than achievement-related goal directedness promotes it. Consistent with this, participants in this study graduated in either 2010 or 2011, a time of high rates of unemployment (Stone, Van Horn, & Zukin, 2012).
Higher levels of achievement goals were associated with an increased likelihood of being in graduate school relative to unemployed. Also, results revealed a significant interaction in the postgraduation occupation status analysis for being in graduate school versus unemployed. To our surprise, follow-up analyses revealed that increased alcohol use over senior year strengthened the positive relationship between achievement goals and the likelihood of being in graduate school 1 year later relative to being unemployed. The association between achievement goals and the likelihood of being in graduate school relative to being unemployed was nonsignificant for those whose drinking decreased over senior year. One interpretation of this unexpected finding is that high achievement goals at the start of the senior year are powerful enough to lead to goal attainment, even in the context of increased drinking that same year, for those who attended graduate school; whereas, increases in drinking exerted a more deleterious effect for those low on achievement goals. Another possibility is that those with high achievement-oriented goals made sufficient advancements in that domain such that they felt freer to drink or pursue goals in other domains in which alcohol may be less interfering (e.g., interpersonal goals). With replication, perhaps with methodologies that include more frequent assessment points, future studies may be able to better delineate the nature of these moderated associations as they unfold over time.
We also considered the timing of some of these goals and developmental tasks in our interpretation of these findings. Broadly speaking, those who apply to graduate school are typically notified regarding the status of their acceptance prior to graduation (some time during the Spring semester), whereas college seniors seeking employment typically begin their search in late spring. Accordingly, one possibility is that those students whose drinking increased over senior year and had higher achievement goals were more likely to be in graduate school than unemployed at T4 because those who were accepted for graduate study may have increased their drinking as their goal already had been attained. However, group means (see Table 3) do not support this alternative explanation. Those who were in graduate school decreased their drinking from fall to spring more than those who were unemployed on average and in relative changes in drinking.
Descriptive Statistics for Outcome Variables (Means and Standard Deviations)
However, perhaps timing is relevant when considering the finding that achievement goals did not predict likelihood of being in graduate school versus unemployed for those whose drinking decreased. This finding also was unexpected. It is possible that within the group of those who were accepted into graduate school, there was individual variability in when participants were informed of their acceptance, which may have influenced spring drinking behavior. Thus, changes in drinking over senior year for those in graduate school may be related to timing of graduate school acceptance. It is also possible that unemployment rates have less to do with alcohol use interference and more to do with other variables (e.g., economy), and thus those in the unemployed group whose drinking reduced may have been making efforts to get a job, but were unable to succeed in this endeavor.
This study raises some interesting questions about goals, task attainment, and drinking in a group that seldom has been studied, graduate students. With 3.8 million people being in graduate school during the 2011–2012 academic year (Ginder & Kelly-Reid, 2013), it may be important to see how those in graduate school differ from those who leave the college environment and enter the workforce. Perhaps there are some fundamental differences for seniors that are preparing to go to graduate school versus those who are preparing to become employed. It may be that failure to moderate alcohol use at specific time points during senior year differentially interferes with successful attainment of occupational milestones such as getting into graduate school or gaining employment. It would be interesting to examine postgraduation lifestyle differences between those in graduate school and those in the workforce, and whether those differences impact changes in drinking during this transition for emerging adults. Such examinations may reveal that many aspects of the emerging adulthood experience, including the length of the developmental period, differs significantly for those who attend graduate school and those who do not.
Summary and Implications
These findings suggest that drinking patterns during the fourth year of college may be particularly impactful on education attainment and other developmentally appropriate goals as college seniors prepare to graduate and transition into the workforce or graduate school. For example, it seems that decreased drinking strengthened the positive effect of personal goals on attainment of some developmental milestones. It is likely that an intervention aimed at reducing the quantity of alcohol consumption during senior year would reduce goal-interfering behavior and perhaps foster more goal-directed behavior. Additionally, an intervention intended to bolster achievement-related goals in the fall of senior year may motivate students to curb their drinking in order to facilitate attainment of those goals. Indeed, research in the area of goal pursuit in college students suggests that goal-directedness can be increased with intervention, and that such increases are associated with a positive impact on developmental milestone attainment (Hoyert, O’Dell, & Hendrickson, 2012). In addition to the applied implications of this work, the current study provides support for examining those who plan to enroll in graduate school or those who are graduate students directly following college graduation as a distinct group of individuals when investigating emerging adults during this transitional period in a research context.
Limitations and Directions for Future Research
The current study had limitations. First is that our study design did not allow us to parse out drinking intentions or the precise temporal ordering of future plans (graduation, employment) and drinking behaviors. For example, it is possible that the students who did not graduate at the end of senior year knew that they were not going to be graduating, and so therefore continued in the college mode and drank heavily, rather than preparing for the transition out of college. Future work will benefit from a more comprehensive baseline assessment of goals, educational aspirations, and timing of goal attainment to better understand the role these may play in the goal-attainment–drinking relationship.
There likely are many aspects of attaining developmentally appropriate goals (e.g., GPA as a measurement of academic achievement, job satisfaction, postgraduation salary, number of promotions), and these are not reflected in our single-item assessment of developmental task attainment. Moreover, it is possible that the categorization approach that we used for developmental task variables was a relatively gross index of these outcomes, and thus did not optimally capture each participant’s goal attainment status (e.g., considered to be a continuing undergraduate due to change of major, considered to be unemployed if completing an unpaid internship postgraduation). Future research can build on the findings that we present here by providing richer and more nuanced assessment of developmental task attainment. In addition to a more detailed assessment of developmental task attainment, a more fine-grained assessment of personal goals would yield greater variability in achievement goal directedness, perhaps allowing for a more refined understanding of these processes across a broad spectrum of goal directedness. Moreover, this type of assessment of goals would allow for more specific information regarding personal goals in other domains.
In addition to limitations regarding the assessment of key variables in this study, it is important to recognize that there are innumerable variables that likely influence the relationship between personal goals and goal attainment and only one of these—alcohol use—was examined in the present study. Other factors include both individual (e.g., expectations, perceived norms, and self-efficacy of goal attainment) and societal (e.g., peer and parental influence) variables not considered in this current study. Though beyond the scope of this study, consideration of some of these other variables and the role that they play in goal attainment during the transition out of college will be an important next step.
Additional limitations are related to sample characteristics and the ability to generalize these findings. Countries may vary with respect to developmental tasks and ages at which individuals might aspire to attain these tasks. Even within the United States, there likely are regional differences in these developmental expectations, or even differences across educational institutions. The small number (n = 10) of participants who were no longer enrolled in college in our sample precluded meaningful examination goal processes for these students. Studies with more diverse samples and at multiple universities (inside and outside of the United States) will help clarify the role of goals and identify goal-interfering factors for those who do not graduate from college.
ConclusionAlthough there has been much focus on the transition into college, there is little research focusing on the transition out of college. Theory and data support that a successful transition out of college is critical for setting a successful trajectory into mature adulthood. Findings from the current study suggest that increased alcohol use during senior year attenuates the link between achievement-oriented goals and graduation from college. However, expected findings for the moderating role of changes in alcohol use over senior year on the goal-attainment association for occupational status after college graduation were not found. These results suggest that there are likely many other variables influencing the association between achievement-oriented goals and postgraduation employment. Future research efforts should focus on how the college environment can serve to enhance and utilize common goals of emerging adults to prepare students for their transition into adult roles. As personal goals were not predictive of postgraduate employment, part of this future research should be aimed at identifying factors which may impede goal attainment, and the development of appropriate interventions that will address these factors and facilitate a successful transition out of college, and into adulthood.
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Submitted: October 31, 2013 Revised: October 20, 2014 Accepted: December 10, 2014
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Record: 71- The role of substance use and emotion dysregulation in predicting risk for incapacitated sexual revictimization in women: Results of a prospective investigation. Messman-Moore, Terri L.; Ward, Rose Marie; Zerubavel, Noga; Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013 pp. 125-132. Publisher: American Psychological Association; [Journal Article] Abstract: Incapacitated sexual assault (ISA) is the most common form of sexual victimization experienced by college women. Although ISA victims are at risk for future assaults, few studies have examined mechanisms responsible for ISA revictimization besides heavy drinking. Using a prospective design, the present study examined whether emotion dysregulation, given its association with interpersonal trauma and substance use, increases risk for revictimization among women with a history of ISA above and beyond the effects of substance use. Female college students (n = 229) completed a baseline assessment followed by assessment of incapacitated sexual assault over a 9-week follow-up period. Approximately 36% of participants reported a history of ISA, and 73% of those victimized during the study had a history of ISA. Revictimized women reported higher levels of alcohol-related problems, greater marijuana use, greater emotion dysregulation, and higher levels of fear and guilt prior to experiencing ISA during the study; however, they did not consume more alcohol than previously victimized women. In a logistic regression analysis, guilt, emotion dysregulation, and marijuana use accurately classified 78.9% of ISA revictimized women. Women with a history of ISA are at substantial risk for ISA revictimization. Findings suggest that even very small increases in emotion dysregulation, particularly in impulsivity, as well as marijuana use, impact revictimization risk substantially. Efficacy of interventions to reduce ISA revictimization may be improved if emotion dysregulation is addressed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Role of Substance Use and Emotion Dysregulation in Predicting Risk for Incapacitated Sexual Revictimization in Women: Results of a Prospective Investigation
By: Terri L. Messman-Moore
Department of Psychology, Miami University;
Rose Marie Ward
Department of Kinesiology & Health, Miami University
Noga Zerubavel
Department of Psychology, Miami University
Acknowledgement: This research was funded by a grant to Terri L. Messman-Moore from the Alcoholic Beverage Medical Research Foundation (ABMRF). ABMRF had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the article for publication. We acknowledge the support of numerous research assistants, whose hard work and dedication made this work possible. Finally, we to express our gratitude to the women who participated and appreciation for their commitment to this prospective study and their willingness to share information about potentially distressing unwanted sexual experiences.
Recent research indicates that alcohol-involved or incapacitated sexual assault (ISA) is widespread. Compared to forcible assault, sexual assault following alcohol or drug consumption is far more common among female college students (Lawyer, Resnick, Bakanic, Burkett, & Kilpatrick, 2010), an experience reported by almost three-quarters of rape victims surveyed in a large, multisite national study (Mohler-Kuo, Dowdall, Koss, & Wechsler, 2004). After entering college, women's risk for ISA increases whereas risk for forcible sexual assault decreases (Krebs, Lindquist, Warner, Fisher, & Martin, 2009). Not surprisingly, women's alcohol use appears to be associated only with ISA and not forcible assault (Testa, Livingston, VanZile-Tamsen, & Frome, 2003). Women's marijuana and other drug use are also associated with ISA, although to a lesser extent than alcohol use (Lawyer et al., 2010). In the only prospective study to examine ISA, monthly binge drinkers were more likely to report incapacitated versus forcible victimization (McCauley, Calhoun, & Gidycz, 2010). Such findings suggest that reducing women's alcohol consumption should decrease risk for ISA (Testa & Livingston, 2009). However, the only relevant study that focused on intervention indicates that reducing women's drinking does not directly diminish revictimization risk among women with previous histories of ISA (Clinton-Sherrod, Morgan-Lopez, Brown, McMillen, & Cowell, 2011). Thus, although women's alcohol use is associated with increased risk of ISA, surprisingly reduction in use is not associated with decreased rates of later revictimization. Although ISA victims are at risk for future assaults, few studies have examined mechanisms responsible for ISA revictimization beyond heavy drinking. More research is needed to understand factors that impact vulnerability for ISA revictimization. Using a prospective design, the present study examined whether emotion dysregulation, given its association with interpersonal trauma and substance use, increases risk for revictimization among women with a history of ISA, above and beyond the impact of alcohol and drug use.
Some women use alcohol to cope with psychological distress arising from sexual assault, such as fear, hostility, or guilt (Miranda, Meyerson, Long, Marx, & Simpson, 2002). For individuals who lack effective emotion regulation skills, negative affect may be experienced as overwhelming, leading to maladaptive behaviors (e.g., substance use) to regulate distress. Although emotion dysregulation is related to negative affect, it is a distinct construct that impacts both emotional experience and behavioral responses (Bradley et al., 2011). Indeed, emotion dysregulation is thought to underlie risky behavior linked to revictimization, including risky sexual behavior and substance use (Filipas & Ullman, 2006). Nonetheless, few studies have explicitly examined emotion dysregulation and revictimization. Among female prison inmates, revictimized women report greater emotion dysregulation, including greater emotional nonacceptance, lack of emotional clarity and awareness, and a greater tendency to engage in impulsive behavior when distressed (Walsh, DiLillo, & Scalora, 2011). Emotion dysregulation is also linked with risky sexual behavior in college women, which predicts revictimization (Messman-Moore, Walsh, & DiLillo, 2010).
Particular components of emotion dysregulation may impact revictimization vulnerability more than others. Lack of emotional awareness or clarity may impair risk perception (e.g., interpretation of fear cues), and thus increase risk for ISA revictimization. Engaging in impulsive behavior in response to negative affect is associated with problematic alcohol use and negative alcohol-related consequences (Magid & Colder, 2007), although it has not yet been examined as a risk factor for revictimization. Elevations in impulsivity may increase the likelihood of entering potentially risky situations, or using alcohol or drugs with unfamiliar companions, which may increase revictimization risk. Because earlier studies examining emotion dysregulation and sexual revictimization used retrospective, cross-sectional designs, it is still unknown whether emotion dysregulation problems actually precede revictimization and whether difficulties in emotion regulation are initially greater among individuals who later become revictimized.
PurposeIdentifying factors that distinguish women who are revictimized from those who are not is essential to the development of effective interventions to prevent revictimization. Moreover, earlier studies failed to examine ISA separately, potentially obscuring the significant impact of victim substance use on risk for ISA (Testa, 2004). Thus, a primary aim of the study is to clarify the role of alcohol use in ISA-related revictimization. It was hypothesized that women's heavy alcohol consumption and alcohol-related problems, as well as drug use, would predict incapacitated sexual revictimization. Another important aim of the study was to test a hypothesis that emotion dysregulation predicts incapacitated sexual revictimization after considering the impact of alcohol use, drug use, and negative affect. Given the predictive nature of the research questions, it was critical to use a prospective design to determine whether substance use and emotion dysregulation preceded ISA. Because of the high prevalence of ISA in college populations (Lawyer et al., 2010), and college women's increased risk for ISA (Krebs et al., 2009), the current study focused on risk factors for ISA revictimization among college women.
Method Participants
Participants were 229 female undergraduate students enrolled at a midsized university in the Midwest. Participants' average age was 19.74 (SD = 1.36, range 18–23). Slightly less than one third (29.3%) were in the first year of college, 14.1% were sophomores, 27.2% were juniors, and 29.3% were seniors. The majority of participants were Caucasian (92.4%) and middle- to upper-class (55.6% reported past year family income of $100K or more). The majority were sexually active (84.7%) and unmarried (95.7%); 48.2% reported being in an exclusive dating relationship.
Measures
ISA
The revised Sexual Experiences Survey (SES; Koss et al., 2007) was administered at Time 1 (the first week of the study) to ascertain ISA (and forcible) victimization experiences from age 14 until entrance into the study. The revised SES was readministered weekly (Times 2–10) to identify prospective ISA victims. For each week, eight different questions assessed unwanted sexual experiences related to respondent alcohol and drug consumption for each of three different types of unwanted sexual contact (kissing/fondling, oral-genital contact, and intercourse). This resulted in 24 variables comprising ISA at each time point. Affirmative responses across the 9-week period were aggregated such that prospective ISA was coded as a dichotomous variable (yes = 1, no = 0).
Child sexual abuse
The Childhood Trauma Questionnaire (CTQ; Bernstein & Fink, 1998) is a 28-item inventory that was administered at Time 1 to determine whether participants had experienced child sexual abuse (CSA) prior to age 14. Participants' scores were classified into categories of abuse severity based on published recommendations; only those individuals reporting moderate to extreme CSA were considered abused. The CTQ has demonstrated reliability and validity (Bernstein & Fink, 1998); internal consistency coefficient for the CSA subscale was .85.
Alcohol use, alcohol-related problems, and drug use
Alcohol consumption was assessed at Time 1 using measures consistent with national studies of college student alcohol use (e.g., CAS; Wechsler & Nelson, 2008). Participants were asked if they had ever consumed alcohol, how old they were the first time they consumed alcohol, their highest number of drinks consumed in a single drinking occasion in the last 30 days, and number of times in the last month that they had consumed four or more drinks in a row on one occasion. In addition, the Alcohol Use Disorders Identification Test (AUDIT; Babor, Higgins-Biddle, Saunders, & Moteiro, 2001) was used to assess alcohol consumption (Questions 1–3; AUDIT-C), as well as symptoms of dependence and alcohol-related problems (Questions 4–10) in the past year. The AUDIT-C shows high levels of sensitivity and specificity in screening for alcohol dependence, any alcohol use disorder (AUD), and risky drinking. Among college students, a cutoff of ≥5 points on the AUDIT-C yields the highest values of sensitivity and specificity for alcohol dependence or risky drinking (Dawson, Grant, Stinson, & Zhou, 2005). In the current sample, internal consistency reliability was .80 for the AUDIT-C and .75 for the dependence/problems subscale. Drug use was assessed with seven questions from the Frequency of Involvement subscale of the Cognitive Appraisal of Risky Events Questionnaire—Revised (CARE–R; Fromme, Katz, & Rivet, 1997; Katz, Fromme, & D'Amico, 2000) measuring frequency of drug use in the previous 6 months.
Negative affect
Negative affect—fear, sadness, hostility, and guilt—was assessed at Time 1 with subscales of the Positive and Negative Affect Schedule (PANAS-X; Bagozzi, 1993). Each subscale score was computed by summing 6 items, which used a 5-point Likert scale from 1 (very slightly or not at all) to 5 (extremely). Internal consistency Cronbach's alpha for the negative affect scales ranged from .79 (hostility) to .89 (sadness) in the current sample. Participants reported on past week emotion, although there is evidence that PANAS scales assess trait affect, which is stable and predictive across extended periods (Watson & Walker, 1996).
Emotion dysregulation
The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) assessed emotion dysregulation at Time 1. The DERS is comprised of 36 items that are summed for a DERS total score or summed into six subscales scores. Higher scores indicate greater emotional dysregulation. All items are scored on a Likert scale from 1 (almost never) to 5 (almost always) and indicate how often the participant experienced the statement. Six subscale scores suggest a lack of emotional awareness characterized by the inability to attend to emotions (Awareness); lack of emotional clarity and personal understanding of emotions (Clarity); failure to accept feeling distressed (Nonacceptance); problems controlling behavior when experiencing negative affect (Impulse); limited access to effective emotion regulation strategies when distressed (Strategies); and difficulties accomplishing tasks when distressed (Goals). Internal consistencies (Cronbach's alpha) for the six scales ranged from .78 (Clarity) to .91 (Goals) in the current sample. Among untreated individuals, DERS scores are relatively stable over 14 weeks (Gratz & Tull, 2010).
Procedure
The committee for human subjects in research approved all procedures. Participants were recruited through fliers posted on campus and advertisements in the student newspaper. The study spanned 10 weeks, with data collection at 1-week intervals. The sample was comprised of four cohorts, each starting the study 1 week apart, in order to stagger the data collection. At the beginning of the study (Time 1), participants completed paper-and-pencil surveys. Women then completed online (Internet-based) surveys weekly (Times 2–9) and returned to complete paper-and-pencil surveys at Time 10. Participant responses were linked via a unique identification number. All in-person data collection took place in group sessions staffed by female research assistants. Participants received an honorarium of $25 for the first session, and were eligible to earn up to $75 for Sessions 2–10 (prorated based upon number of surveys completed, up to $50, with an additional $25 for completion of all 10 surveys). Following participation each week, women received information regarding counseling and support services, as well as researcher contact information.
Data Analysis
All analyses were conducted with SPSS 18. Chi-square, analysis of variance (ANOVA), and multivariate analysis of variance (MANOVA) were conducted to identify relevant ISA revictimization risk factors. These initial analyses included the entire sample to examine whether differences existed among revictimized women, previously victimized but not revictimized women, and nonvictims on study variables. To predict revictimization, logistic regression analyses were conducted with the subset of women with a prior history of ISA. All variables for which revictimized women differed from previously victimized women in the previous analyses were included as predictors in the two logistic regression analyses (i.e., with emotion dysregulation broadly defined and subtypes of emotion dysregulation).
Results Study Retention and Handling of Missing Data
Participants in the study were drawn from a larger sample of 424 female undergraduate students. Participants completed an average of 9.12 (SD = 1.61) weekly sessions; 85.8% (n = 364) of participants completed all 10 weekly sessions or only missed one session. Missing data on ISA victimization was minimal; 93.9% of the sample was missing less than 5% (96.7% missing less than 10%). Women with missing data who reported ISA were classified as ISA victims; women with missing data who did not indicate ISA were unable to be unequivocally classified as nonvictims and were excluded from analyses. There were no significant differences between the subset of women who provided complete victimization data (n = 255) and those who did not (n = 169) on study variables (ps > .05). Missing data on other variables was random and minimal (less than 5% missing on any remaining variables). To maximize sample size, analyses were conducted casewise. Distributions of predictor variables were normal and without significant outliers.
Prevalence of ISA and Revictimization
Incapacitated sexual revictimization occurred if a woman with a prior history of ISA (regardless of history of CSA or forcible sexual assault) experienced another episode of ISA during the 9-week follow-up period. Of the 255 individuals with complete victimization data (see Figure 1), 7.5% (n = 19) reported CSA or forcible sexual assault after age 14 but did not report incapacitated assault. Given the focus on risk for ISA revictimization, these women were excluded. Among the remaining 236 participants, 92 women reported a history of ISA: 8.1% (n = 19) reported prospective revictimization (ISA prior to as well as during the 10-week study) and 30.9% (n = 73) reported prior ISA without prospective victimization. In addition, 2.9% (n = 7) reported ISA during the 10-week study (i.e., prospective victimization) but had no prior ISA victimization. Given the focus on factors that predict ISA revictimization, these women were excluded from subsequent analyses, resulting in a final sample of 229 women.
Figure 1. Participant flowchart. Shaded blocks indicate participants excluded from analyses. ISA = incapacitated sexual assault; CSA = child sexual abuse.
Among those who experienced ISA revictimization, 78.9% (n = 15) of cases involved only alcohol, 10.5% (n = 2) involved alcohol and other substances, and 10.5% (n = 2) involved other substances but not alcohol. Prior ISA was significantly associated with prospective ISA, χ2(1) = 17.19, p < .001, Φ = .26; 20.7% of women with a history of ISA reported prospective ISA and 73.1% of women who reported ISA during the study (i.e., were prospectively victimized) had a history of ISA. There was no association between CSA and prior ISA, χ2(1) = 0.64, p = .43, nor between CSA and ISA during the study, χ2(1) = 0.62, p = .43. There were no differences in rates of ISA or revictimization among the cohorts (ps > .48).
Alcohol and Other Substance Use
The average peak drinking occasion in the previous month was 6.13 (SD = 4.14, range 0–20), and participants reported consuming four or more drinks on one occasion (heavy episodic drinking) almost twice in the previous month (M = 1.84, SD = 1.41). The average AUDIT-C score was 5.01 (SD = 5.28), just exceeding the cut score of 5 which identifies college students with any AUD, alcohol dependence, and risky drinking practices as defined by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (Dawson et al., 2005). In the previous 6 months, 9.8% of the sample reported using marijuana at least once. Drug use other than marijuana was very infrequent; participants who reported using drugs other than marijuana also reported using marijuana, therefore analyses including drug use focused on marijuana use only.
Differences Between Revictimized and Nonrevictimized Individuals
Demographics
Revictimized individuals did not differ from previously victimized or nonvictimized individuals in terms of age, race, or cohort.
Substance use and related problems
A MANOVA was conducted to determine whether, prior to experiencing ISA revictimization during the study, participants differed in terms of alcohol consumption and alcohol-related problems. The multivariate test was significant, Wilks' Λ = .75, F(8, 402) = 7.88, p < .001. All follow-up ANOVA tests were significant (p < .001; see Table 1). Women with a history of ISA (with or without revictimization) reported higher levels of alcohol consumption and alcohol-related problems than nonvictimized women. Revictimized women did not report greater consumption than previously victimized women but did report significantly higher levels of alcohol-related problems. A chi-square analysis also indicated that revictimized women were more likely to exceed the AUDIT-C cut-score for probable alcohol dependence (Dawson et al., 2005), and nonvictims were less likely than expected to exceed this cut-off. Revictimized women were 3.68 times more likely to report marijuana use than expected; the other groups' actual use did not differ from expected values.
Differences Between Revictimized and Nonrevictimized Groups on Alcohol Use Variables
Emotion dysregulation and negative affect
Two analyses (ANOVA and MANOVA) were conducted to determine whether participants differed in terms of emotion dysregulation and negative affect prior to experiencing ISA revictimization. An ANOVA indicated differences on the DERS total score between the revictimized group and the previously victimized and nonvictimized group, F(2, 217) = 7.46, p = .001 (see Table 2). The MANOVA examined differences among groups for the DERS subscales and negative affect. The multivariate test was significant, Wilks' Λ = .84, F(20, 406) = 1.87, p < .05. Follow-up univariate ANOVA tests were significant for impulse, clarity, goals, fear, and guilt (see Table 2). Post hoc comparisons indicated revictimized women reported greater difficulties inhibiting impulsive behavior, greater fear, and greater guilt compared to previously victimized and nonvictimized women. There were no significant group differences for emotional clarity or goals, or between the previously victimized groups for strategies or hostility. Effect sizes for differences between revictimized and previously victimized women were in the medium range (Cohen's ds ≥ .5) for negative affect and in the large range (Cohen's ds ≥ .8) for DERS total score and difficulties in impulse control.
Differences Between Groups on Emotion Dysregulation and Negative Affect
Logistic Regression Analyses Predicting Prospective Revictimization
To identify risk factors that distinguish women at risk for ISA revictimization, all women with a history of ISA (n = 92) were included in hierarchical logistic regression analyses to examine predictors of prospective ISA (i.e., ISA revictimization). For all analyses, variables were selected for entry into the equation based on Wald forward estimation in three steps: (a) AUDIT scores for alcohol-related problems and marijuana use, (b) negative affect (fear and guilt), and (c) emotion dysregulation (total score, impulsivity subscale).
The first analysis (Model 1) was conducted to determine whether emotion dysregulation (assessed by DERS total score) predicted risk for revictimization after controlling for alcohol problems, marijuana use, and negative affect (see Table 3). Each block was significant; in the third block, marijuana use and DERS total predicted revictimization (guilt was not significant). The nonsignificant Hosmer-Lemeshow test for Block 3 indicated good model fit, χ2(8) = 8.42, p = .39, −2 log likelihood = 68.42, Nagelkerke R2 = .30. The predictors accurately classified 73.2% of revictimized women and 70.6% of nonrevictimized women, with an overall classification rate of 72.7%. In the second analysis, which included only one aspect of emotion dysregulation, impulsivity, each block was significant. In the third block, revictimization was predicted by marijuana use, guilt, and impulsivity. The nonsignificant Hosmer-Lemeshow test for Block 3 indicated good model fit, χ2(8) = 8.25, p = .41, −2 log likelihood = 69.93, Nagelkerke R2 = .35. The predictors accurately classified 78.9% of revictimized women and 76.1% of nonrevictimized women, with an overall classification rate of 76.7%.
Emotion Dysregulation Predicting the Probability of Incapacitated Sexual Assault Revictimization
DiscussionDespite earlier evidence linking ISA victimization with heavy episodic drinking (McCauley et al., 2010), in the current study alcohol consumption did not distinguish women at risk for ISA revictimization when considered with other factors. Revictimized women were more likely than expected to exceed the threshold for heavy consumption indicative of alcohol dependence (Dawson et al., 2005), and revictimized women did report more alcohol-related problems and dependence symptoms than did previously victimized women. However, neither of these alcohol-related variables predicted revictimization risk when considered with marijuana use, negative affect, and emotion dysregulation. ISA victimized women (both those who were and were not revictimized) reported very high levels of heavy episodic drinking, with AUDIT-C scores in the clinical range. Thus, the ubiquitous nature of heavy drinking among women with a history of ISA may have prevented consumption variables from emerging as significant predictors of revictimization. Such findings are consistent with a recent study indicating that reductions in women's drinking did not decrease subsequent revictimization risk (Clinton-Sherrod et al., 2011). The present findings, in conjunction with earlier studies, suggest that heavy drinking or problematic consumption may be a risk factor for ISA in general, rather than ISA revictimization. In contrast to alcohol use, marijuana use was a significant risk factor for ISA revictimization. Drug use, including marijuana use, has been associated with revictimization in earlier investigations (Casey & Nurius, 2005; Messman-Moore, Ward, & Brown, 2009). It is unknown how marijuana use may increase risk, as women's drug use is not often associated with sexual assault in event-based studies (Ullman, Karabatsos, & Koss, 1999). Perhaps marijuana use is a marker of deviant behavior or a deviant peer group, which may increase the likelihood of encountering sexually aggressive men. Additional studies with longer follow-up periods are needed to examine alcohol use and to determine how marijuana and other drugs may contribute to ISA revictimization.
Emotion dysregulation predicted the likelihood of incapacitated sexual revictimization after accounting for the impact of alcohol-related problems, marijuana use, and negative affect. Impaired emotion regulation likely interferes with a woman's ability to appraise or cope with dangerous situations, thereby impeding appropriate self-protective or escape responses when at imminent risk for revictimization (Dietrich, 2007). Although a global construct of emotion dysregulation predicted ISA revictimization, difficulties inhibiting impulsive behavior when distressed appear to be especially problematic. Individuals who have difficulty controlling impulsive behavior may be at greater risk for ISA revictimization because they do not pause to identify risk or because they lack the capacity to effectively negotiate risky situations. Impulsive women may be more likely to enter high-risk situations or may be more likely to engage in risky drinking practices (e.g., drinking quickly, consuming shots/drinks with high alcoholic content) that increase the likelihood of incapacitation. Findings suggest that even very small increases in emotion dysregulation, particularly impulsivity, increase revictimization risk substantially.
Guilt also predicted ISA revictimization during the study, but only when examined with impulsivity rather than emotion dysregulation broadly defined. Guilt and self-blame are almost universal reactions to sexual assault, and guilt tends to be even more pronounced among revictimized women (Breitenbecher, 2001). Among survivors of interpersonal violence, guilt is associated with increased levels of distress, and predicts avoidant, maladaptive coping—which often includes heavy drinking or drug use (Street, Gibson, & Holohan, 2005). Although guilt is not associated with weekly or daily drinking (Hussong, Hicks, Levy, & Curran, 2001), problem drinkers tend to experience heightened levels of negative self-awareness including guilt (Hull, 1981). Women may be particularly vulnerable to revictimization if they are heavy drinkers and experience high levels of guilt.
This is the first prospective study to examine emotion dysregulation as a predictor of ISA revictimization, yet its findings must be considered in the context of some limitations. Given the small sample, predictors of incapacitated and forcible revictimization could not be examined, as only two individuals reported forcible assault in the absence of incapacitation. Although earlier research suggests that alcohol and drug use by victims is typically predictive of incapacitated rather than forcible assault (e.g., Testa, 2004), more research in this area is needed to clarify whether emotion dysregulation is as relevant to forcible sexual assault. Findings may also be impacted by the relatively short follow-up period (9 weeks). Future studies should aim to balance frequency of participation and potential participant fatigue (i.e., number of questions assessing victimization) with longer follow-up periods. Even in the current brief 10-week study, some participants did not complete all questions, rendering victimization status inconclusive for a significant number of women, and it is not clear how the exclusion of these individuals may have affected the findings reported here. Other factors, such as peak BAC and other aspects of risky drinking should also be assessed to increase our understanding of ISA revictimization. Factors associated with emotion dysregulation and substance use also should be examined, such as PTSD. Given that less than 4% of college women meet criteria for sexual assault-related PTSD (Read, Ouimette, White, Colder, & Farrow, 2011), and that college women likely have lower levels of emotion dysregulation when compared to nonclinical samples, such questions may best be answered with more diverse community and clinical samples, increasing generalization of findings reported here. Yet college women are appropriate for study given the high rates of heavy alcohol consumption and ISA in this population (Krebs et al., 2009; Lawyer et al., 2010).
The present prospective study is a significant first step in establishing the relevance of emotion dysregulation as a precursor of incapacitated sexual revictimization. The good news is that emotion regulation skills can be taught, and emotion dysregulation can improve with tools and practice. Interventions designed to promote women's safety and reduce revictimization risk should aim to enhance emotion regulation skills that may reduce risk among the most vulnerable women.
Footnotes 1 It is impossible to determine whether participants who leave items blank on the SES have experienced unwanted sexual activity. Because recalling experiences of sexual assault can be distressing, some women may have been motivated to skip particular questions or participation in a week following an unwanted sexual experience. To be conservative, these individuals were excluded because they could not be labeled as nonvictims.
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Submitted: September 23, 2011 Revised: August 17, 2012 Accepted: October 16, 2012
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Record: 72- The structure of client language and drinking outcomes in project match. Martin, Tim; Christopher, Paulette J.; Houck, Jon M.; Moyers, Theresa B.; Psychology of Addictive Behaviors, Vol 25(3), Sep, 2011 pp. 439-445. Publisher: American Psychological Association; [Journal Article] Abstract: Client language during Motivational Interviewing interventions is an important predictor of drinking outcomes, but there are inconsistencies in the literature regarding what aspects of client language are most predictive. We characterized the structure of client language by factor analyzing frequency counts of several categories of client speech. The results provide limited support for a model proposed by Miller et al. (2006) and Amrhein et al. (2003) but with some important differences. While Amrhein et al. (2003) found that only increasing strength in client commitment language predicted behavior change, the current study revealed that client language preparatory to commitment predicted drinking outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Structure of Client Language and Drinking Outcomes in Project MATCH
By: Tim Martin
Department of Psychology, Kennesaw State University;
Paulette J. Christopher
Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico
Jon M. Houck
Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico
Theresa B. Moyers
Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico
Acknowledgement: This research was supported in part by Department of Defense Grant DAMD 17-01-1-0681, National Institute on Alcohol Abuse and Alcoholism RO1 AA 13696 01 and National Institute on Drug Abuse R01 DA 13801. The authors wish to thank J. Scott Tonigan for statistical consultation for this project.
Theresa B. Moyers acts as a consultant/trainer for motivational interviewing through a private consulting business.
Client language is increasingly recognized as an important predictor of clinical outcomes for motivational interviewing (MI). Evidence is accumulating for a predictive role for particular elements of client speech in behavioral change (Amrhein, Miller, Yahne, Palmer, & Fulcher, 2003; Hodgins, Ching, & McEwin, 2009; Moyers et al., 2007; Gaume, Gmel, & Daeppen, 2008). Specifically, language that the client offers during an MI treatment session weighing in favor of changing a problematic behavior, typically substance abuse, predicts posttreatment drug and alcohol use, even when the level of initial motivation, severity of dependence, and efficacy for change have been accounted for (Moyers, Martin, Houck, Christopher, & Tonigan, 2009).
Miller and Rollnick (1991, 2002) have drawn upon self-perception theory (Bem, 1967) to explain how these client statements for or against change may influence client motivation in MI sessions. If the client argues in favor of change (change talk, CT), the client perceives that what he or she is arguing for must be what he or she believes, thereby increasing motivation for change. In other words, “the person literally talks himself or herself into change.” (Miller & Rollnick, 2004, p. 300). Alternatively, when clients argue against change (counter-change talk, CCT), their perception of the self making this argument lowers motivation to change. This implies that client speech that favors change (CT) should predict favorable outcomes, while client speech supporting the target behavior (CCT) should predict maintaining the status quo. These predicted relationships between CT, CCT, and outcome have now been observed several times, but with a number of inconsistencies. Miller, Benefield and Tonigan (1993) found that client verbalizations of resistance to change, or CCT, predicted client drinking outcome assessed 12 months after therapy. In the same study, however, Miller et al. failed to find a significant relationship between outcome and CT. Moyers et al. (2007) found that both CT and CCT independently predicted drinking behavior averaged over a period of 10–15 months after therapy.
In an influential study, Amrhein et al. (2003) conducted an analysis of client language in a randomized clinical trial for MI with drug-using clients. Based on a priori hypotheses concerning the nature of social commitments, Amrhein et al. (2003) conceptualized several sub-categories of change talk, including Commitment, Desire, Ability, Need, Readiness, and Reasons. In addition to categorizing certain acts of client speech, coders rated the strength (i.e., intensity) of CT and CCT utterances. They found that drug use outcomes were associated with the pattern of these strength ratings during MI treatment sessions. Specifically, they found that increasing strength of Commitment statements predicted more favorable drug use outcomes. Based largely upon this research, a model of client speech was developed in which expressions of change talk categorized as statements of Desire, Ability, Reasons, and Need, collectively termed “preparatory language,” should lead to statements of commitment to change a problematic behavior. These commitment statements should then predict posttreatment behavior (Miller, Moyers, Amrhein, & Rollnick, 2006). Thus, preparatory language and commitment language are seen as two distinct constructs. The clinical implications of this model are straightforward, that is, clinicians should hesitate to move forward with action strategies (called Phase II in MI) until commitment language is strong, regardless of how many statements of desire, ability, reason, and need have been offered.
Research subsequent to the original Amrhein study has been consistent in supporting the value of change talk in predicting clinical outcomes in MI, although evidence for the dominance of commitment language has been mixed. For example, Gaume, Gmel, and Daeppen (2008) found no link between commitment language and drinking outcomes using the Motivational Interviewing Skills Code (MISC) 2.0 (Miller, Moyers, Ernst, & Amrhein, 2003), but did find an association between drinking outcomes and client statements of ability to change. Similarly, Baer et al. (2008) found that client statements about reasons to change were associated with reductions in substance use in homeless adolescents, though commitment language was not. Moyers et al. (2007) found that a single, generic change talk category predicted drinking outcomes in a secondary analysis of Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) outcomes, without reference to commitment language. On the other hand, Hodgins, Ching, and McEwen (2009) found that commitment language predicted gambling outcomes in a randomized controlled trial using MI, while preparatory language did not.
The consistent finding of a relationship between client speech and outcome is promising, but the inconsistencies in what aspect of speech is most predictive points to the need for a more complete understanding of the structure of client verbalizations. Does client speech naturally cleave into preparatory and commitment categories as suggested by Amrhein et al. (2003), or would clinicians be better advised to attend to any and all statements in favor of change when considering whether to move forward to action planning in MI sessions?
This study attempts to inform this question by examining the underlying structure in a large sample of client speech drawn from Motivational Enhancement Therapy sessions in Project MATCH (Project MATCH Research Group, 1997). Therapy sessions were recorded and evaluated using the sequential code for observing process exchanges (SCOPE; Martin et al, 2005). The SCOPE was developed in response to a perceived need to investigate the dynamics of therapy sessions (Moyers & Martin, 2006). SCOPE combines elements of the MISC 1.0, particularly codes for therapist speech, and also incorporates multiple categories of client preparatory and commitment language using definitions similar to those of Amrhein et al. (2003). The SCOPE coding system also includes a procedure for recording the serial order of client and therapist statements so that sequential patterns can be analyzed statistically. Results of the sequential analysis of client-counselor interactions for this project are given elsewhere (Moyers & Martin, 2006; Moyers et al., 2009). This paper, however, focuses on the frequency counts of client speech coded from these tapes. Because multiple categories of both CT and CCT are measured, these frequency counts provide an opportunity to investigate the structure of change talk. By treating each category frequency as an imperfect indicator of one or more latent constructs, the underlying structure of those constructs can be estimated using factor analysis. Thus, even if the categories do not perfectly coincide with the actual psychological sources of the speech events, so long as each category is distinct enough to blend these underlying factors in a different way (i.e., to be a different linear combination of underlying factors), and broad enough to capture several of the underlying factors that contribute to client motivational speech, factor analysis should give some indication of this latent structure. To this end we factor analyzed frequency counts of client speech from 118 Project MATCH interviews from the data set reported in Moyers et al. (2009). For the purposes of this analysis, we restricted ourselves to the first session of the MET condition from Project MATCH (Project MATCH Research Group, 1997).
We reasoned that if preparatory language and commitment statements are distinct constructs as hypothesized by Miller et al. (2006), we would expect two factors to explain client language both for and against change. One factor would include commitment to change and commitment to maintain the status quo. A second factor would be expected to account for statements of Desire, Ability, Reasons, or Need to change or maintain the target behavior. A third factor would be expected to account for neutral client speech (i.e., unrelated to the target behavior), coded in SCOPE as Follow (a client utterance unrelated to the target behavior) and Ask (client asks a question). We would also expect the commitment factor, but not the preparatory language or neutral factors, to predict drinking outcomes.
Method Participants
The data selected for this analysis were from 118 first-session tapes from the MI condition of Project MATCH (Project MATCH Research Group, 1997). We restricted this analysis to first-session tapes for several reasons. First, we restricted the analysis to a single session because the effect of sessions on client and therapist behavior is largely unknown and beyond the scope of this analysis. The first session was chosen because it represented the largest sample of sessions available to us, and because in the past frequency counts of initial sessions have been found to correlate with outcome using other coding instruments (Moyers et al., 2007) and the SCOPE (Moyers et al., 2009). Details of the overall sample are given elsewhere (Project MATCH Research Group, 1997). In the subsample reported here, 91 clients (77%) were male, 86 (72.9%) were White, 11 (9.3%) were African American, 20 (16.9%) were Hispanic, and 1 (0.8%) was of another ethnicity. The mean age was 40.75 (range 21–74), and the mean number of years of education was 13.53 (range 8 – 20). All study and consent procedures were reviewed and approved by the human research Institutional Review Board of the University of New Mexico.
Coding
Therapy sessions were coded using the SCOPE (Martin et al., 2005). The manual for SCOPE is available from http://casaa.unm.edu/download/scope.pdf. The coding process has been described in detail elsewhere (Moyers & Martin, 2006; Moyers et al., 2009). Briefly, audio recordings of therapy sessions were transcribed and then assessed in two separate passes. Coders both listened to the recording and read along with the transcript for both passes, marking their codes directly in the transcript. In the first pass the recording was parsed into utterances, which were defined as expressions of a single idea. In the second pass, each utterance was assigned a single category code, based on definitions found in the coding manual. Typically each pass was performed by a different coder. There were 16 categories of client speech. These categories were Follow and Ask (described above) to describe speech unrelated to the target behavior, and Desire, Ability, Reason, Need, Taking Steps, Commit, or Other. To specify direction (i.e., reflecting movement toward change or the status quo), these categories were followed by a “+” or “−” symbol. For example, “Reason +” would refer to a reason to change, while “Reason−” would denote a reason to maintain the target behavior.
Data Analysis
This is a secondary analysis of data reported elsewhere (Moyers & Martin, 2006; Moyers et al., 2009). Frequency counts of client speech as coded by SCOPE were factor analyzed, using principal components extraction, a retention criterion of 1 eigenvalue, and varimax rotation. Principal components extraction was chosen because it includes variance unique to each measured variable (Harris, 1975; Johnson & Wichern, 1982), and there was no evidence of large differences in communalities among the measured variables (Harris, 1975, pg. 223), which ranged from 0.526–0.75. This implies that methods that exclude unique variance would not improve the solution, but would degrade the relationship between empirical data and factor scores (Harris, 1975, pp. 222–223). Varimax rotation (Kaiser, 1958) was implemented because it tends to result in more interpretable factors than the unrotated (principle component orientation) solution, but will be highly similar to the unrotated solution if the observed correlation matrix is caused predominantly by a single latent variable.
Drinking outcome measures have been described in detail elsewhere (Project MATCH Research Group, 1997). Briefly, we used proximal and distal measures of percent days abstinent (PDA) and drinks per drinking day (DDD). In Project MATCH, PDA was assessed using the Form-90. The Form-90 incorporates memory cues from time-line follow-back procedures with drinking pattern estimation methods from the Comprehensive Drinker Profile (Miller & Del Boca, 1994; Miller & Marlatt, 1984).These measures are averaged across follow-up assessments. The proximal measures are averaged across assessments conducted at months 4–9 post-therapy, while the distal measures are averaged across months 10–15 posttherapy. To improve the normality of the distributions, PDA was arcsine transformed and DDD was square-root transformed. Two-step hierarchical multiple regressions were used to predict these criterion variables. This was done to assess whether client speech predicted unique variance in outcome in addition to known predictors. In the first step of each regression model, a baseline measurement of the criterion variable, as well as Alcohol Involvement (AIM) as measured by a third-order scale from the Alcohol Use Inventory (Wanberg, Horn, & Foster, 1977), self-efficacy as measured by the Alcohol Abstinence Self-Efficacy scale (AASE: DiClemente, Carbonari, Montgomery, & Hughes, 1994), and readiness to change were entered. Readiness to change was derived from the University of Rhode Island Chance Assessment Scale (URICA: McConnaughy, Prochaska, & Velicer, 1983) by summing that instrument's Contemplation, Action, and Maintenance subscales and subtracting the Precontemplation subscale score (Carbonari, DiClemente, & Zweben, 1994; Connors, Tonigan, & Miller, 2001). In the second step, the six factors of the FA were entered.
ResultsDetails regarding interrater agreement of the SCOPE codes used here are given in detail elsewhere (Moyers et al., 2009). Briefly, agreement for the frequency counts of client speech used here were assessed with intraclass correlation coefficients (Shrout & Fleiss, 1979), and ranged from 0.620 (Commit−) to 0.993 (Ask). “Other−” (arguments in favor of maintaining the target behavior that were not classifiable elsewhere), was removed from the factor analysis because its ICC was 0.229, unacceptably low (Cicchetti, 1994).
The correlation matrix for client speech is given in Table 1. The table is provided for readers who may be interested in exploring other factor models of the data. Factor loadings are given in Table 2. The 16 categories of client speech were characterized by six factors with eigenvalues > 1.0 explaining 64.85 % of the variance. Variables loading most heavily on Factor 1 included Commit−, Desire−, Reasons−, and Need−. We suggest that this factor be interpreted as reflecting motivation to maintain the status quo. Factor 2, which included both Steps + and Steps−, as well as Need−, might reflect actions related to drinking behavior generally, rather than movement in a specific direction toward or away from change. Factor 3 included Desire+, Reason+, Need+, and Other + speech. We interpret this as reflecting preparatory language as described by Amrhein et al. (2003), but without the Ability category. Instead, Ability + was split between the next two factors. Factor 4 includes Commit + and Ability+, as well as Follow. We suggest that this factor reflects commitment to change. Factor 5 has strong positive loadings for both Ability + and Ability−. This factor may reflect ambivalence on the part of many clients, who tend to express concurrently their ability to change and the difficulty they anticipate in doing so. The final factor is straightforward, with Follow and Ask loading most heavily. This factor likely reflects the client's general participation in the session.
Correlations Among CT and CCT Frequencies
Factor Loadings of Client Speech Variables
The hierarchical regressions of DDD were not significant. The hierarchical regression of proximal PDA on baseline measures and factors is given in Table 3. The first step, which included baseline PDA, AIM, AASE, and Readiness, was significant, F(4, 93) = 5.655, p < .0005. Only baseline PDA was a significant predictor of proximal PDA. The change in R2 at the second step was not statistically significant, ΔR2 = 0.097, p = .077, but Factor 2 was nevertheless a significant predictor of proximal PDA. The model overall was significant, F(10, 87) = 3.59, p = .001, adjusted R2 = 0.21, SE = 0.39.
Multiple Regression of Proximal PDA on Baseline PDA and Factors
The regression of distal PDA is given in Table 4. The model at the first step was significant, F(4, 97) = 5.44, p = .001. Only baseline PDA was a significant predictor. The change in R2 at the second step was significant, ΔR2 = .112, F(6, 87) = 2.32, p = .04, as was the overall model, F(10, 87) = 3.75, p < .0005, adjusted R2 = 0.22, SE = 0.43. Factor 3 and Factor 5 were significant predictors, with a positive and negative slope respectively.
Multiple Regression of Distal PDA on Baseline PDA and Factors
DiscussionThe results of the factor analysis provide limited support for the two-construct theory of client speech proposed by Miller et al. (2006). There were factors that could be interpreted as preparatory language and commitment to change, although categories of counter-change talk did not cleave so cleanly between preparatory and commitment categories. Additional factors indicate that more than two constructs are necessary to account for client speech related to change.
Frequency of Desire+, Reasons+, Need+, and Other + loaded positively on Factor 3, the Preparatory Language Factor. This factor is largely consistent with the two-construct model and has a positive slope with distal PDA, indicating that as clients express more of these preparatory statements, PDA increases. However, client language about ability to change did not load onto this factor. Instead, the frequency of language regarding the ability to change appears to reflect two independent factors. Ability + statements were primarily associated with Factor 4 (Commit+, Ability+). This close link between commitment to change and perceived (or at least verbalized) ability to change may reflect an increased likelihood to commit to change only with a sufficiently high confidence in one's ability to be successful.
Client speech categorized as Follow also loaded on Factor 4, although not as heavily as it loaded on Factor 6 (Follow, Ask). The frequency of this category, which is explicitly defined as speech not related to the target behavior or neutral with respect to the target behavior, will reflect several characteristics of the client, therapist, and situation, including trait talkativeness, therapeutic alliance, and the degree to which clients are willing to follow the topical lead of the therapist. Any one of these (and perhaps others), alone or in combination, could explain why Follow would load on the same factor as Commit + and Ability+. It could be that high levels of alliance are globally associated with overall talkativeness in a session but selectively associated with verbalizations of commitment and ability to change. Perhaps more simply, it could be that clients who have already committed to changing the target behavior (and thus will emit more Commit + statements) also tend to be more talkative during therapy, and thus the relationship between Follow and Commit + merely reflects this relationship.
Factor 5 appears to reflect a more general concept of ability, in that both Ability + and Ability− loaded positively. In other words, clients who expressed an ability to change also tended to express doubts or reservations about their ability to change. The interpretation of this factor is not necessarily straightforward. Ability− loaded most heavily on Factor 5 and the slope of the relationship between it and distal PDA was significantly negative, meaning that higher scores on this factor predicted fewer abstinent days. However, because Ability + also loaded positively on Factor 5, it does not appear to indicate only a perceived inability to change. It may instead reflect ambivalence about one's ability to change, with poor outcome associated with high ambivalence. Within motivational interviewing sessions, then, clinicians should not be surprised to hear clients expressing both confidence and doubt about a change.
Similarly, Factor 2 was composed of Steps+, Steps−, and Need−. This factor is somewhat puzzling. Steps are defined in the SCOPE as reports of active changes that a person has made in his or her life to either support the target behavior or change it. For example, a person might start taking aspirin before going to bed to avoid a hangover (Steps−) or change their driving patterns to avoid a tempting bar (Steps+). The fact that the frequency of these categories is positively correlated is therefore an interesting finding in itself and merits further investigation. The combination of these statements with Need−, a stated lack of need to change the target behavior, may be an indicator of a particular stage of change. Once the target behavior has been changed, for example, one would not expect to continue hearing Need + statements. Therefore, this factor may reflect variation between clients in the current state of their attempts to change their target behavior, with those who have successfully reduced or eliminated the behavior commenting on steps taken, both forward and back, and lacking in statements reflecting a current, immediate need for change. Those who have not yet successfully begun or made the change, on the other hand, may not report concrete steps toward or away from change, but express more Need + statements reflecting their recognition that a current need to change exists. This interpretation is consistent with the relationship between this factor and proximal PDA. Those further along the continuum of change at the first therapy session would be high on Factor 2, and would achieve higher levels of PDA in the first few months after therapy, while those who were still in early stages of change would be low on Factor 2, and might well take more time to achieve abstinence, if they ever do. The fact that Factor 2 predicts unique variance in proximal PDA in the presence of baseline PDA as a predictor strengthens the interpretation that this factor reflects the process of change and not only current behavior. The fact that it did not predict distal PDA (p = .078) may reflect a real reduction in influence over time, or merely measurement error in the presence of marginal statistical power.
Three of the derived factors predicted drinking outcomes as measured by percent days abstinent (PDA). Factor 2 (Steps+, Steps−, Need−) was positively associated with Proximal PDA, while Factor 3 (Desire+, Reason+, Need+, Other+) was positively associated with Distal PDA. In contrast, Factor 5 (Ability+, Ability−) was negatively associated with Distal PDA. The association of the Preparatory Language factor (Factor 3) with outcome is consistent with the result of Baer et al. (2008), who found that statements of reasons to change were positively associated with changes in substance use in adolescents. In addition, both Baer et al. (2008) and Gaume et al. (2008) found that statements of ability/inability to change were associated with outcome, consistent with our finding of a relationship between the Ability factor (Factor 5) and outcome.
The implication of these results for clinicians using MI is that rather than a strict focus on the strength of client language, clinicians may adopt a broad focus on the general concept of change talk and how prevalent it is in the MI session, at least within the first therapy session. Our data suggest that clinicians may not need to differentiate between categories of change talk “on the fly” during treatment sessions, but can respond to any offer of change talk on the part of the client without the need for belabored examination. Additional clinician attention is warranted only when counterchange talk occurs more often than does change talk, particularly within the categories of Ability and Steps. If replicated, this result will also call into question the concept of two distinct phases of therapy, a preparatory followed by an action phase. However, we hasten to add that our sample was restricted to first sessions, and so these results may not generalize beyond an initial session. In some cases the action phase may not emerge until later therapy sessions, and commitment language during those sessions may well predict outcome as well or better than preparatory language did in the current report.
Despite limited support for the two-construct model, our data present a few surprises that merit some discussion. First, client language about ability to change does not reflect the same factor as statements of desire, need, and reasons to change, contrary to the predictions of the two-construct model. The closest relationship is found between ability statements and the factor reflecting commitment to change.
Perhaps more important than the number of factors, the pattern of predictive factors is at odds with expectations from the two-construct model. While the Preparatory Language factor (Factor 3) itself is somewhat consistent with the two-construct model, the fact that it accounts for unique variance in outcome in the presence of Commitment (Factor 4) is not. The Steps factor is positively associated with proximal PDA, and the Ability factor is negatively associated with distal PDA. Both of these factors appear to represent a dichotomy, with the direction of relationship with outcome determined by the valence of the more frequent utterance within the category. For example, in the Taking Steps factor, there were nearly three times as many Taking Steps + utterances as there were Taking Steps−, and this factor was positively associated with proximal PDA. In contrast, in the Ability factor there were nearly twice as many Ability− utterances as there were Ability+, and this factor was negatively associated with distal PDA.
There are several possible reasons for the discrepancy between our findings and the two-construct model. The first is that the coding definitions within a two-construct model differ in at least one important way from those of the SCOPE, as evidenced by the examples given in their report. Specifically, many instances that Amrhein et al. (2003) would classify as Commit would be coded in SCOPE as Reasons. Therefore it is likely that a great many of the statements that Amrhein et al. classified as Commit are here categorized as Reason + or Reason−. Other possible reasons for the discrepancy include the fact that the samples, the therapy protocols, and the coding and analysis methods of the studies are different.
Another important difference between the SCOPE and the two-construct model is in how frequencies are counted. Amrhein et al. (2003) collapsed across the change-status quo dimension, so that the frequency of a category like Commit would include both “I am going to change” and “I am not going to change.” It is this frequency count that failed to discriminate between outcome clusters in their report. The factor structure found here indicates why frequency might not predict behavioral outcomes when collapsed across this dimension. Verbalizations of CT and CCT in general load on different factors, indicating that while conceptually (and statistically) related, CT and CCT are empirically distinguishable.
There are several limitations to the current study. The selection of therapy sessions for coding was not random, but depended instead on the willingness of individual IRB committees at Project MATCH sites to approve a secondary analysis (Moyers et al., 2009), and this may limit our ability to generalize to the population of people treated for substance abuse. This was a secondary analysis of data in which client speech was known to predict outcome (Moyers et al., 2009), which may have led to some degree of alpha inflation. The fact that strength of utterances was not coded limits our ability to compare results directly with others who do so (e.g., Amrhein et al., 2003; Gaume et al., 2008), although it simultaneously extends our knowledge to another measure of client speech that should be equally well covered by the theoretical constructs in question. The inability of the factors to predict DDD indicates that they likely cannot predict all outcome measures with equal power, and may suggest limited construct validity. Despite these limitations, the results of this study provide strong evidence that two constructs are not sufficient to account for client speech related to change, and some indication of what a more adequate framework for understanding client speech might look like. Further analyses of client language in studies of similar populations with similar coding systems will be an important addition to the literature on the mechanisms of effectiveness in MI. Factor 5 (Ability) is particularly intriguing, as it is conceptually related to self-efficacy and autonomy, concepts considered critical to MI effectiveness (Miller & Rollnick, 2002) and indeed to the wider issue of intrinsic motivation (Deci & Ryan, 1985; Ryan & Deci, 2000). We believe that uncovering these mechanisms is worthwhile, as they should lead to greater efficacy and effectiveness of MI as well as improved efficiency in its delivery.
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Submitted: June 29, 2010 Revised: January 14, 2011 Accepted: January 18, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (3), Sep, 2011 pp. 439-445)
Accession Number: 2011-08219-001
Digital Object Identifier: 10.1037/a0023129
Record: 73- The suitability of the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) as a screening tool: IRT-based evidence. Chiesi, Francesca; Donati, Maria Anna; Galli, Silvia; Primi, Caterina; Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013 pp. 287-293. Publisher: American Psychological Association; [Journal Article] Abstract: The South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) is one of the most widely used measures of adolescent gambling. We aimed to provide evidence of its suitability as a screening tool applying item response theory (IRT). The scale was administered to 981 adolescents (64% males; mean age = 16.57 years, SD = 1.63 years) attending high school. Analyses were carried out with a sample of 871 respondents, that is, adolescents who have gambled at least once during the previous year. Once the prerequisite of unidimensionality was confirmed through confirmatory factor analysis, unidimensional IRT analyses were performed. The 2-parameter logistic model was used in order to estimate item parameters (severity and discrimination) and the test information function. Results showed that item severity ranged from medium to high, and most of the items showed large discrimination parameters, indicating that the scale accurately measures medium to high levels of problem gambling. These regions of the trait were associated with the greatest amount of information, indicating that the SOGS-RA provides a reliable measure for identifying both problem gamblers and adolescents at risk of developing maladaptive behaviors deriving from gambling. The IRT-based evidence supports the suitability of the SOGS-RA as a screening tool in adolescent populations. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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The Suitability of the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) as a Screening Tool: IRT-Based Evidence
By: Francesca Chiesi
Department of Psychology, University of Florence, Italy;
Maria Anna Donati
Department of Psychology, University of Florence, Italy
Silvia Galli
Department of Psychology, University of Florence, Italy
Caterina Primi
Department of Psychology, University of Florence, Italy
Acknowledgement:
Large-scale international prevalence studies have revealed that between 66% and 86% of youth reported gambling in the past year (Hardoon, Gupta, & Derevensky, 2004), and although there is a lack of consensus as to the actual adolescent prevalence of severe gambling problems, there is a general agreement on the fact that a high proportion of adolescents gamble excessively and that, as a group, adolescents constitute a high-risk population for developing gambling problems (Derevensky & Gupta, 2006; Jacobs, 2004).
Due to the societal importance of the phenomenon, much attention has been paid to the issue of measurement of youth gambling problems (for reviews, see Derevensky & Gupta, 2004, 2006), and there is a debate about the efficiency of the most commonly employed adolescent gambling screens, such as the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA; Winters, Stinchfield, & Fulkerson, 1993); the Massachusetts Adolescent Gambling Screen (MAGS; Shaffer, LaBrie, Scanlan, & Cummings, 1994); and the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) adapted for juveniles (DSM-IV-J; Fisher, 1992), and its revised version, the multiple-response format for juveniles (DSM-IV-MR-J; Fisher, 2000). These tools have been adapted from adult instruments, modifying some items to make them more age appropriate. These items focus on the behavioral indicators of problem gambling (lying, chasing), the emotional and psychological correlates of pathological gambling (withdrawal, guilt, preoccupation, loss of control), the adverse consequences of excessive gaming (illegal acts, school or work problems), and the economic and social problems directly associated with gambling (excessive losses, family problems).
Among these measures, the SOGS-RA (Winters et al., 1993), despite its widespread use (for a recent review, see Blinn-Pike, Worthy, & Jonkman, 2010), has been criticized for overdiagnosing problem gambling compared with other popular adolescent gambling severity measures. Comparison studies between the SOGS-RA, the MAGS, and the DSM-IV-J have indicated that the SOGS-RA may provide overly liberal estimates of problem gambling among adolescents (Derevensky & Gupta, 2000; Langhinrichsen-Rohling, Rohling, Rohde, & Seeley, 2004). Nonetheless, a screening tool has to identify individuals who are problem gamblers (i.e., those who exhibit maladaptive behaviors related to gambling) as well as at-risk gamblers (i.e., those who are at risk of developing maladaptive behaviors). Thus, the SOGS-RA might represent an efficient screening tool for identifying adolescents with gambling-related problems as well as individuals who are potentially at risk. Indeed, a population screen is expected to overidentify false-positives (Sharp et al., 2012).
Starting from this premise, it seems relevant to further explore the suitability of the SOGS-RA as a screening tool for at-risk and problem gamblers. Indeed, although the SOGS-RA is widely used to classify adolescents into gambling problem severity categories, there is no empirical evidence of its effectiveness in making accurate distinctions between these categories (Hardoon, Derevensky, & Gupta, 2003; Langhinrichsen-Rohling et al., 2004; Olason, Sigurdardottir, & Smari, 2006; Wiebe, Cox, & Mehmel, 2000). The present study aimed to address this issue by applying item response theory (IRT), as there is a general lack of IRT studies on problem gambling severity measures in general and, in particular, on the SOGS-RA for adolescents. To the best of our knowledge, only a very recent study (Sharp et al., 2012) applied IRT to the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001), a screen for adults, and Molde and colleagues (Molde, Pallesen, Bartone, Hystad, & Johnsen, 2009) applied IRT to the MAGS (Shaffer et al., 1994) in a prevalence study among adolescents in Norway.
In order to provide evidence of the efficiency of the SOGS-RA as a screening tool, we aimed to assess the measurement precision of the scale across severity levels of problem gambling through the test information function (TIF). Instead of providing a single value (e.g., coefficient alpha) for reliability, IRT recognizes that measurement precision can be different at different levels of the trait (Embreston & Reise, 2000; Hambleton, Swaminathan, & Rogers, 1991), and the TIF is used to evaluate the precision of the test at different levels of the measured construct. When estimating the SOGS-RA's reliability at different levels of problem gambling, we expected to find the SOGS-RA to discriminate highly within the regions representing impaired levels from at-risk gambling to severe problem gambling. That is, in line with recent results obtained applying IRT to the PGSI (Sharp et al., 2012), we expected to find an information function that rises in the appropriate area of the latent trait distribution.
Because the information provided by a test depends on the properties of the test items, the characteristics of the 12 items of the SOGS-RA were investigated. IRT allows for obtaining item difficulty/severity and discrimination parameters that describe these properties. The difficulty of an item indicates where the item functions along the trait, and it can be interpreted as a location index with regard to the trait being measured. For example, a less difficult item functions among the low-trait respondents and a more difficult item functions among the high-trait respondents. Discrimination describes how well an item can differentiate between examinees with levels of trait below the item location and those with levels of trait above the item location. Thus, given that the SOGS-RA should be highly discriminating within some regions representing impaired levels from at-risk gambling to severe problem gambling, we expected to find the majority of the item severity parameters to be located in this particular area of the latent trait distribution and to be associated with high discrimination parameters.
Method Participants
Participants were 981 14- to 20-year-old adolescents (64% males; mean age = 16.57 years, SD = 1.63 years) attending four high schools in a suburban area in Italy (Tuscany). The sample was recruited by presenting the project to the schools' headmasters. Schools were randomly selected, and, once the schools agreed to participate (from the six schools that were contacted, two declined to participate because they were involved in other projects), a detailed study protocol that explained the study's goal and methodology was approved by the institutional review boards of each school. Students received an information sheet, which assured them that the data obtained from them would be handled confidentially and anonymously, and they were asked to give written informed consent. Parents of minors were required to provide consent for their child's participation. All the youth invited to participate agreed to do so. Thus, sample bias during recruitment was minimized.
Measure and Procedure
The SOGS-RA (Winters et al., 1993; Italian version, Bastiani et al., 2010) derives from the South Oaks Gambling Screen (SOGS) for adults (Lesieur & Blume, 1987). Because the scale has been criticized for the ambiguity of some items, and because of its susceptibility to acquiescence bias (Ladouceur et al., 2000), several researchers have provided evidence of the psychometric properties of the SOGS-RA, confirming its single-factor structure (Boudreau & Poulin, 2007; Olason et al., 2006; Winters et al., 1993), internal consistency (e.g., Boudreau & Poulin, 2007; Olason et al., 2006; Skoukaskas, Burba, & Freedman, 2009; Winters et al., 1993), and criterion and construct validity (Derevensky & Gupta, 2000; Olason et al., 2006; Poulin, 2002; Skoukaskas et al., 2009).
The scale is composed of two sections. The first one consists of unscored items investigating gambling behavior. Specifically, these items assess if the respondent has ever participated (never, at least once) in any of 11 gambling activities (card games, coin tosses, bets on games of personal skill, bets on sports teams, bets on horse or dog races, bingo, dice games for money, slot machines, scratch cards, lotteries and online games), the relative frequency (never, less than monthly, monthly, weekly, daily) of participation during the last year in these gambling activities, and the amount of money spent on gambling. The second section is composed of 12 items assessing the severity of problem gambling (see Table 1). All items require dichotomous answers (i.e., yes or no) except the first item, which has a 4-point response scale (never, some of the time, most of the time, every time), and it is dichotomized (i.e., never/some of the time or most of the time/every time) in the scoring phase. The SOGS-RA was presented in a paper-and-pencil version and it was collectively administered during school time. Administration time was about 15 min.
Percentages of Affirmative Answers, Standardized Factor Loadings, Fit Statistics, and Parameters for Each Item of the SOGS-RA
ResultsWith regard to the first section, results showed that 11% of the participants indicated that they never gambled, whereas 871 respondents had gambled at least once during the last year. From the latter group, 470 students were nonregular gamblers (52% males), that is, they participated from less than monthly to less than weekly in at least one gambling activity in the last year, whereas 401 students (77% males) were regular gamblers, that is, they participated weekly or daily in at least one gambling activity in the last year. The most common activities were scratch cards (75%), playing cards for money (74%), and lotteries (57%), whereas the least common ones were online games (16%) and bets on horse or dog races (7%).
Regarding the second section, as expected (e.g., Boudreau & Poulin, 2007; Winters et al., 1993), the percentage of item endorsements was low, with the exception of Items 4 and 6, which, in line with previous studies (Govoni, Frisch, & Stinchfield, 2001; Wiebe et al., 2000), had higher endorsement rates.
Some inconsistency in the SOGS-RA cutoff scores used to categorize adolescent gambling behavior exists (Blinn-Pike et al., 2010). However, a score of 4 or more has been used to indicate a problem gambler; a score of 2 to 3, an at-risk gambler; and a score of 0 to 1, a nonproblem gambler (Winters, Stinchfield, & Kim, 1995). According to these criteria, in the current study, we found 662 (76%) nonproblem gamblers (with scores of 0 or 1), 147 (17%) at-risk gamblers (with scores of 2 or 3), and 62 (7%) problem gamblers (with scores of 4 or more). Their mean SOGS-RA scores were 0.28 (SD = .45), 2.26 (SD = .45), and 5.55 (SD = 1.81), respectively.
Because the administration of the SOGS-RA was precluded in the case of 110 adolescents who declared that they did not gamble in the past year, IRT analyses were carried out on a sample of 871 adolescents. As a preliminary step, the one-factor structure of the scale was tested through categorical weighted least squares confirmatory factor analysis implemented in the Mplus software (Muthén & Muthén, 2004). The results indicated that a single-factor model adequately represents the structure of the SOGS-RA. Specifically, the comparative fit index (CFI) and the Tucker-Lewis index (TLI) were .96 and .97, respectively, and the root mean square error of approximation (RMSEA) was .03, indicating an excellent fit (Schermelleh-Engel & Moosbrugger, 2003). Factor loadings were all significant (p < .001), ranging from .53 to .83 (see Table 1).
Having verified the assumption that a single continuous construct accounted for the covariation between item responses, unidimensional IRT analyses were performed. The two-parameter (2PL) logistic model was tested in order to estimate the item severity and discrimination parameters. The 2PL model is the most commonly used IRT model in clinical assessment (for a review, see Thomas, 2011) and, in particular, it is a suitable model to analyze measures designed to assess maladaptive habit severity, as suggested by recent studies in this area (e.g., Hagman & Cohn, 2011; Molde et al., 2009; Sharp et al., 2012; Srisurapanont et al., 2012).
Parameters were estimated by employing the marginal maximum likelihood (MML) estimation method with the EM algorithm (Bock & Aitkin, 1981) implemented in IRTPRO software (Cai, Thissen, & du Toit, 2011). In order to test the adequacy of the model, the fit of each item under the 2PL model was tested computing the S−χ2 statistics. Given that using larger samples results in a greater likelihood of significant chi-square differences, the critical value of .01 rather than the usual critical value of .05 was employed (Stone & Zhang, 2003). Each item had a nonsignificant S-χ2 value (see Table 1), indicating that all items fit under the 2PL model, that is, both the severity and the discrimination parameters described the properties of the SOGS-RA items. Concerning the severity parameters (b), the results showed that parameters ranged from 1.03 ± .09 to 2.63 ± .25 logit across the continuum of the latent trait (see Table 1). Only Items 4 and 6 had low b values, indicating that these symptoms were the least severe within the continuum of problem gambling, whereas the remaining 10 items all had values ≥2 (when rounded), referring to more severe symptoms distributed from 1.96 ± .09 to 2.63 ± .25 logit. Concerning the discrimination parameters (a), following Baker's (2001) criteria, 10 out of 12 items showed large (a values over 1.34) discrimination levels, with Items 11 and 12 being the most discriminating ones, and only Items 2 and 7 had medium (a values ≤1.34) discriminatory power (see Table 1). For illustrative purposes, Figure 1 shows the item characteristics curves used in IRT to provide visual information of the item characteristics. Severity is represented by the location of the curve along the trait. All items were located in the positive range of the trait, indicating the regions where they function better. Discrimination is represented by the steepness of the curve. The steeper the curve, the better the item can discriminate. All items showed a high slope, indicating their ability to distinguish between respondents with different levels of trait around their location.
Figure 1. The ICCs of each item of the SOGS-RA under the 2PL. Latent trait (Theta) is shown on the horizontal axis and the probability of endorsing the affirmative response option is shown on the vertical axis. ICC = item characteristics curve; SOGS-RA = South Oaks Gambling Screen–Revised for Adolescents; 2PL = two-parameter model.
The TIF estimated under the 2PL model showed that the instrument was sufficiently informative for mid- to high levels of severity (see Figure 2). Within the range of trait from 1.00 to 3.00 standard deviations above the mean (fixed by default to 0), the amount of test information was ≥4, indicating that the instrument was sufficiently informative. More specifically, the amount of test information was >6 starting from a trait level of 1.50, and the TIF peaked at 10 at the trait level of 2.2, where the measurement precision of the SOGS-RA was the highest. Referring to the summed score to ability score conversion table provided by IRTPRO, applying the expected a posteriori (EAP) methods for summed scores (Thissen & Orlando, 2001), the trait level of 0.99 corresponded to a summed score of 2, the trait level of 1.54 to a summed score of 3, and the trait level of 1.97 to a score of 4. As we described, these summed scores represent the cutoff scores employed to classify respondents into at-risk and problem gambler categories. Thus, IRT analyses attested that the SOGS-RA was quite accurate in identifying these categories as well as in discriminating between them.
Figure 2. Test information function of the SOGS-RA under the 2PL model. Latent trait (Theta) is shown on the horizontal axis, and the amount of information and the standard error yielded by the test at any trait level are shown on the vertical axis. SOGS-RA = South Oaks Gambling Screen–Revised for Adolescents; 2PL = two-parameter model.
DiscussionAn effective screening tool is expected to be simple and efficient, with a short administration time. It should be designed to measure youth problem gambling and to identify individuals who are at risk of developing problem behavior (Derevensky & Gupta, 2000). The present results provide evidence that the SOGS-RA satisfies these requirements.
First, in line with previous studies (Boudreau & Poulin, 2007; Olason et al., 2006), the scale was found to be unidimensional. This is a desirable characteristic, as a single-factor structure facilitates the scale's function as a population screen of problem gambling, and because a screening tool is not expected to measure the subtleties and complexities associated with a multidimensional behavioral disorder (Derevensky & Gupta, 2004).
Second, IRT provides clear evidence of the good performance of each item of the SOGS-RA and of the global scale in measuring adolescent problem gambling, as well as in identifying both adolescents who are exhibiting problem behaviors and adolescents for whom gambling represents a potential source of risk. Indeed, the scale's precision was higher from middle to upper levels of the trait, indicating that the SOGS-RA cutoff scores used to define categories of gamblers (i.e., scores from 2 to 3 indicating at-risk gamblers, and scores of 4 or more indicating problem gamblers) are reliable. Moreover, the fact that more information is provided in the upper portion of the trait continuum is consistent with results recently obtained applying IRT to a problem gambling screen employed with adults (Sharp et al., 2012), and more generally with IRT findings of other clinical screens (e.g., Aggen, Neale, & Kendler, 2005; Sharp, Goodyer, & Croudace, 2006).
Specifically, item properties (i.e., severity and discrimination) were consistent with the aim of measuring problem gambling efficiently. With regard to severity, the majority of the items were located along the range of values that the SOGS-RA aims to measure accurately, and the described symptoms (lying, chasing losses, guilt, loss of control, borrowing money, school and family problems) are consistent with the essential features of pathological gambling as defined in the last editions of the DSM (DSM-IV, American Psychiatric Association, 1994; text revision, DSM-IV-TR, American Psychiatric Association, 2000). Only two items (feeling bad about the amount of money lost, gambling more than planned) had low values, indicating that these symptoms were the least severe among problem gamblers, as attested by previous studies reporting that these two items were endorsed more frequently than the others (Govoni et al., 2001; Wiebe et al., 2000). Concerning discrimination, the parameter estimates indicated that the items of the SOGS-RA were able to distinguish between the different levels of the trait. In particular, items referring to skipping classes or being absent from school due to betting activities, and borrowing money or stealing something in order to bet or to cover gambling bets, were the most discriminating ones. The items with the lowest discrimination power were lying about winning and wanting to stop gambling, but not thinking to be able to, suggesting that these symptoms represent less distinctive signs of maladaptive gambling.
The following limitations of our study should be noted. First, as with all self-report questionnaire-based studies, our findings may have been affected by response bias (such as acquiescence or social desirability) and by how confident participants were that their answers would be kept confidential. Another limitation is the way our sample was recruited. As we invited schools to participate, our participants were all adolescents attending high schools. Thus, students who dropped out of school or working adolescents were not included. Despite these limitations, the current study contributes to the literature investigating the psychometric properties of the SOGS-RA and provides evidence of its suitability for screening purposes.
Footnotes 1 IRT has been largely applied in the development of measures of ability and achievement. In this field, the term “difficulty” is the more suitable to define the characteristic of the items. From a clinical standpoint, “difficulty” can be best conceptualized as “severity” of the symptom described by the item. For this reason, in the present article, we employ the term “severity,” referring to the SOGS-RA.
2 Parameters are expressed on a log-odd scale and the units are called logits. The logit is the logarithm of the odd, that is, the ratio between the probability of answering “yes” and the probability of answering “no.”
3 Because information is equal to the inverse of the standard error, higher values indicate higher accuracy.
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Submitted: April 23, 2012 Revised: July 26, 2012 Accepted: July 26, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 287-293)
Accession Number: 2012-26453-001
Digital Object Identifier: 10.1037/a0029987
Record: 74- Thresholds of probable problematic gambling involvement for the German population: Results of the Pathological Gambling and Epidemiology (PAGE) Study. Brosowski, Tim; Hayer, Tobias; Meyer, Gerhard; Rumpf, Hans-Jürgen; John, Ulrich; Bischof, Anja; Meyer, Christian; Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015 pp. 794-804. Publisher: American Psychological Association; [Journal Article] Abstract: Consumption measures in gambling research may help to establish thresholds of low-risk gambling as 1 part of evidence-based responsible gambling strategies. The aim of this study is to replicate existing Canadian thresholds of probable low-risk gambling (Currie et al., 2006) in a representative dataset of German gambling behavior (Pathological Gambling and Epidemiology [PAGE]; N = 15,023). Receiver-operating characteristic curves applied in a training dataset (60%) extracted robust thresholds of low-risk gambling across 4 nonexclusive definitions of gambling problems (1 + to 4 + Diagnostic and Statistical Manual for Mental Disorders-Fifth Edition [DSM-5] Composite International Diagnostic Interview [CIDI] symptoms), different indicators of gambling involvement (across all game types; form-specific) and different timeframes (lifetime; last year). Logistic regressions applied in a test dataset (40%) to cross-validate the heuristics of probable low-risk gambling incorporated confounding covariates (age, gender, education, migration, and unemployment) and confirmed the strong concurrent validity of the thresholds. Moreover, it was possible to establish robust form-specific thresholds of low-risk gambling (only for gaming machines and poker). Possible implications for early detection of problem gamblers in offline or online environments are discussed. Results substantiate international knowledge about problem gambling prevention and contribute to a German discussion about empirically based guidelines of low-risk gambling. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Thresholds of Probable Problematic Gambling Involvement for the German Population: Results of the Pathological Gambling and Epidemiology (PAGE) Study
By: Tim Brosowski
Institute of Psychology and Cognition Research, University of Bremen;
Tobias Hayer
Institute of Psychology and Cognition Research, University of Bremen
Gerhard Meyer
Institute of Psychology and Cognition Research, University of Bremen
Hans-Jürgen Rumpf
Department of Psychiatry and Psychotherapy, University of Lübeck
Ulrich John
Institute of Social Medicine and Prevention, University Medicine Greifswald
Anja Bischof
Department of Psychiatry and Psychotherapy, University of Lübeck
Christian Meyer
Institute of Social Medicine and Prevention, University Medicine Greifswald
Acknowledgement: This study was funded by the Senator for Health of Bremen.
In the course of the last decades, gambling became an essential component of leisure activities (Petry, 2004). Technologies like the Internet, smart phones, or online social networks further accelerate the public dominance of gambling offers and challenge regulation authorities by their ubiquitous character (Wood & Williams, 2009). In view of diverse negative public health consequences of excessive gambling (e.g., individual and social costs like bankruptcy, impaired psycho-social functioning, or disease-related costs), there is a need of manifold evidence-based means of harm reduction and health promotion that provide a broad framework for corporate social responsibility and the prevention of gambling-related harm.
Applications of Consumption Measures in Gambling ResearchIn contrast to the field of gambling research, alcohol research has a long lasting tradition of analyzing data of consumption behavior (Rehm et al., 2003; Room, 2000). The core idea is that patterns of consumption behavior are reliably associated with particular outcomes like drinking-related harms or beneficial effects (Burger, Brönstrup, & Pietrzik, 2004; Rehm et al., 2003). Assessing such patterns of consumption incorporates cultural norms, beverage types, personal characteristics, or temporal variations like occasions of heavy involvement (Single & Leino, 1998). Despite the fact that biochemical measures like blood alcohol are unique to substance use, most of the quantitative measures of consumption may be adapted to gambling research (Currie & Casey, 2007).
A crucial proxy of consumption patterns is the consumed amount aggregated across some period of time (Room, 2000; Single & Leino, 1998). Several studies established a dose-response-relationship between such measures of alcohol drinking intensity and different drinking-related harms (Burger et al., 2004; Rehm et al., 2003). In line with this research, there is evidence that the intensity of gambling behavior also shows a dose-response-relationship with a positive association of higher intensity and higher probability of related problems (Currie, Hodgins, Wang, el-Guebaly, Wynne, & Chen, 2006). Consequently, evidence about robust relations of individual consumption and associated harms may provide levels of low-risk gambling and will enrich responsible gambling strategies (Currie & Casey, 2007; Currie, Hodgins, Wang, el-Guebaly, & Wynne, 2008).
To date, the concept of a dose-response-relationship between levels of gambling intensity and gambling-related problems has been applied in different populations and with different aims. For instance, thresholds of probable problematic gambling involvement were extracted in a sample of college students (Weinstock, Whelan, & Meyers, 2008), in a mixed sample of community and outpatient individuals (Quilty, Avila Murati, & Bagby, 2014) and levels of moderate posttreatment gambling reliably distinguished problem-free from symptomatic gamblers (Weinstock, Ledgerwood, & Petry, 2007). To the authors’ knowledge, the only thresholds of potentially harmful gambling involvement extracted from representative surveys were provided and repeatedly evaluated for Canadian population samples (Currie et al., 2012, 2006, 2008; Currie, Miller, Hodgins, & Wang, 2009). Currie et al. (2006) established following last year intensity thresholds associated with gambling-related harms (defined as at least two negative consequences of gambling during the last year; items from the Canadian Problem Gambling Index [CPGI]): (a) gambling no more than two to three times per month, (b) spending no more than $501–1,000CAN per year on gambling, and (c) spending no more than 1% of gross family income on gambling activities. In a survey among 171 gambling experts (Currie et al., 2008) the majority agreed on the value of empirically based quantitative limits of low-risk gambling in combination with other gambling-related guidelines and population-level interventions. Despite these promising first steps explicit evidence about potential limits of low-risk gambling is scarce. In absence of a gambling-related equivalent to the standard-drink (Currie & Casey, 2007), behavioral measures of risk-assessment and thresholds of low-risk gambling behavior have to account for national characteristics of products (availability, acceptance, and regulation) by the application of current and representative training samples to calibrate the thresholds of the applied instruments (Schellinck & Schrans, 2011). Beyond the heuristic value of thresholds of low-risk involvement for problem gambling prevention, there is an ongoing debate about the potential risks posed by particular game types because of inherent structural or contextual characteristics (Griffiths, 1993; Meyer, Fiebig, Häfeli, & Mörsen, 2011; Welte, Barnes, Tidwell, & Hoffman, 2009) and some experts emphasize the importance of game-specific thresholds of harmful gambling involvement (Currie et al., 2008). Quilty et al. (2014) empirically extracted such form-specific thresholds of gambling intensity in a nonrepresentative sample of 275 Canadian gamblers and pointed out the importance of further research in this topic with other and larger samples.
Aims of the Study
Against the background of the outlined state of research, the aims of this article are threefold: The first aim is to extract and cross-validate evidence-based thresholds of probable low-risk gambling involvement for the German gambling market in a current and representative dataset. German guidelines of responsible gambling may benefit from evidence-based cutoffs of low-risk gambling involvement. Following the examples of Currie et al. (2006, 2008, 2009) this study, for the first time, presents thresholds of low-risk gambling intensity from a representative but non-Canadian dataset.
Currie et al. (2006) excluded all individuals from their analyses who (a) did not report gambling at least once in the last year and who (b) did not answer all CPGI questions related to harm (individuals who did not gamble more than one to five times per year and self-identified as being a nongambler were not administered the questions related to gambling problems). Cunningham (2006) criticized this preselection procedure particularly because of the exclusion of many individuals from answering the CPGI items because of self-identifying as nongamblers (including respondents who were frequent gamblers). He supposed many false-negative cases, and therefore, decreased representativeness, reliability, and validity of the analyses. It is plausible that (a) a less case-sensitive preselecting filter before presenting the problem gambling items causes too many false-negative problem gamblers in the subsample of presumable nonproblematic individuals; (b) consequently raises the mean level of gambling intensity in the sample of presumable nonproblematic individuals; and (c) therefore, increases the thresholds of gambling intensity that reliably distinguish probable problematic gamblers from low-risk gamblers. According to this criticism, problem gambling items in the study at hand were only administered to individuals that gambled more than 10 days during their lifetime, an empirically derived filter that showed 100% sensitivity for pathological lifetime gamblers (Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition [DSM–IV]). Because of the problematic implications of the missing data Cunningham (2006) recommended systematic replication of research in thresholds of low-risk gambling. This study is the first to do so in a non-Canadian representative dataset.
The second aim of the study is to evaluate how the absolute size of the thresholds is influenced by (a) analyzing the entire unrestricted sample (gamblers and nongamblers) or by (b) reducing the analyzed sample on more involved gamblers (last year gamblers only). It is plausible that the second condition increases the mean level of gambling intensity and consequently the absolute cutoff values. This study will provide evidence to make an informed choice between both conditions and to stimulate further research.
The third aim is to extract and cross-validate form-specific thresholds of probable problematic gambling involvement and to discuss their potential for early detection of problem gamblers in gambling venues or online environments. Analytical conditions of the extracted thresholds vary across different timeframes, levels of problem gambling outcomes and dimensions of gambling intensity to provide a broad and evidence-based foundation for subsequent informed choices and practical applications.
Method Dataset
All analyses are based on a representative dataset of the German population (Pathological Gambling and Epidemiology [PAGE]). A detailed description of the study design and fieldwork may be found elsewhere (Meyer et al., 2015). In a computer-assisted telephone interview conducted between June 2010 and January 2011, 14- to 65-year old participants were asked about their gambling behavior and leisure activities. The survey was based on a nationwide representative, stratified, and clustered random sample of 15,023 participants. The sampling included a random digit dialing procedure that was adapted to the German system of allocation of telephone numbers. To maximize coverage, two sampling frames of landline and mobile phone numbers were applied (for other recent examples of such dual-sampling frames also see: Bundeszentrale für gesundheitliche Aufklärung [BZgA], 2014; Jackson, Pennay, Dowling, Coles-Janess, & Christensen, 2014).
To embrace claims of stability and predictive accuracy of the extracted thresholds (Currie et al., 2009; Quilty et al., 2014), the values were extracted from a training dataset (about 60% of the nonweighted cases, randomly chosen from each stratum of the 1–4 levels of DSM–5 lifetime symptoms of gambling disorder) and were validated in a test dataset (the complementary 40% of the nonweighted cases from each stratum). Because of random sampling, the exact sample sizes differ slightly.
DSM–5 definition of gambling disorder explicitly refers to the occurrence of symptoms within the last year. From an early detection perspective thresholds of low-risk gambling involvement that only cover recent behavior patterns may be of particular interest because of pragmatic issues of real-time observability, documentation, legal aspects of privacy, and data protection. However, thresholds that cover a lifetime perspective may be of particular interest from a preventive perspective, because of claims of generalizability of low-risk gambling involvement across the entire life period. Variables in the PAGE-dataset are based on DSM–IV and refer to both last year and lifetime timeframe. Consequently, analyses were conducted on both timeframes separately but not across timeframes.
Dependent Variables (Outcomes)
Gambling problems were assessed with the gambling section of the World Mental Health (WMH) Composite International Diagnostic Interview (CIDI) Version 3.0 published by the World Health Organization (WHO, 2009). The interview assessed the 10 lifetime symptoms of pathological gambling in DSM–IV. Nine of the 10 symptoms were summed up to create an additive index of problem severity ranging from 0 to 9 (the DSM–IV item about criminal behavior was dropped to apply the new DSM–5 definition of gambling disorder). To incorporate a broad subclinical and clinical definition of problem gambling (Currie et al., 2009), the number of lifetime DSM–5 criteria was recoded into the following dummy variables of nonexclusive outcome groups: (a) at least one DSM–5 criterion across the lifetime, (b) at least two criteria across the lifetime, (c) at least three criteria across the lifetime, and (d) at least four criteria across the lifetime. To identify participants with current gambling problems, we assessed the recentness of the last gambling-related symptom. If a symptom was present within the past year participants were allocated to the following nonexclusive last year outcome groups: (a) at least one DSM–5 criterion across the lifetime and at least one gambling problem-related symptom present within the last year, (b) at least two criteria across the lifetime and at least one gambling problem-related symptom present within the last year, (c) at least three criteria across the lifetime and at least one gambling problem-related symptom present within the last year, and (d) at least four criteria across the lifetime and at least one gambling problem-related symptom present within the last year. The DSM–5 criteria of the gambling section were only presented to individuals who reported a lifetime gambling frequency of more than 10 days. In a pretest of 673 lifetime gamblers this filter item showed 100% sensitivity for pathological lifetime gamblers (5–10 DSM–IV lifetime criteria). In general, item nonresponse was low (average missing rate for all 16 CIDI symptom questions: 0.14%; with 49.3% coded as “don’t know” and 51.7% as “refused” answers). In line with commonly used diagnostic algorithms of the CIDI, cases with postfilter missing values in the dummy coded DSM outcomes were recoded as “0” (no problem).
Independent Variables (Predictors)
The PAGE dataset comprises active gambling days across the following 21 game types: Lotto 6/49 (weighted prevalence of the entire sample: lifetime = 50%; last year = 30%), Spiel77/Super 6 (32; 19), class lotteries, (9; 2), German TV lottery (9; 3), instant lotteries (28; 11), Keno (4; 1), Quicky (1; 0.2), other lotteries (16; 6), TOTO (2; 1), ODDSET (5; 2), other sports betting (4; 2), horse race betting (5; 1), casino table games (9; 2), casino slot machines (6; 2), poker (6; 4), gaming machines in amusement arcades, restaurants or pubs (17; 5), bingo (4; 2), TV quiz channel gambling (7; 3), trading on the stock exchange (2; 1), and private/illicit gambling (3; 2). All types are documented for lifetime (nominal level: never, 1–10 days, 11–50 days, 51–100 days, 101–500 days, 501–1,000 days, or more than 1,000 days) and last year (metric level: 0–365 days) timeframes. In the following active gambling days in particular game types (lifetime or last year) are termed “form-specific” predictors.
In line with Currie et al. (2006), a composed index of active gambling days was computed by extracting the maximum value of active days across all game types (lifetime as well as last year). This variable only regards the most favored game type (the type, a gambler was most involved in) and disregards that an individual may be involved in several types on one active day and, therefore, provides a conservative proxy of overall gambling involvement (Cunningham, 2006). The second composed index was the number of game types involved in (lifetime as well as last year) to provide a second proxy measure that compensates the weakness of the first. The third predictor across all game types was the maximum amount of money (€) lost within 1 year of a lifetime (open answer; only available for lifetime outcomes). In the following the three aggregated predictors of activities across all game types (lifetime or last year) are termed “composed” predictors. Missing values in all predictor variables were recoded like the CIDI outcome variables.
Other Variables (Covariates)
In line with Currie et al. (2006) and Currie, Hodgins, Wang, el-Guebaly, Wynne, and Miller (2008), the validation analyses included sociodemographic covariates, which also influence the applied problem gambling outcomes (see Meyer et al., 2011): gender, age, employment status (unemployed; full- or part-time employed), migration background (German; mother, father or oneself not German), and education (three ascending levels: ([1] no graduation or secondary school up to 9 years, [2] secondary school up to 10 years, and [3] general or vocational diploma).
Data Analyses
All data analyses were conducted with PASW Statistics 18. To account for the complex sampling design and to reduce potential bias from selective nonresponse, all analyses were based on weighted data and the estimation of SEs in logistic regressions was adjusted for clustered sampling. In a first step in the training dataset, each predictor variable of a timeframe was applied to each outcome variable of the corresponding timeframe to extract area under the curve (AUC) values, sensitivity, specificity, and the optimal cutoff of the predictor with equal weight to sensitivity and specificity. The AUC values comprise combined information of sensitivity and specificity of a predictor variable for a dichotomous outcome with values between 0.5 and 1 and higher values reflecting higher accuracy (for a detailed discussion of this procedure see: Swets, Dawes, & Monahan, 2000). Both objects of the study (prevention guidelines and early detection heuristics) benefit from this approach of balancing sensitivity and specificity, because of challenges of false alarm, stigmatizing, and blunting. In a second step, the extracted thresholds of indicators with useful accuracies (AUC >0.7; see Swets, 1988) were inspected for their concurrent validity by logistic regressions in the test dataset that also incorporated sociodemographic confounders like gender, age, education, employment status, and migration. These adjustment procedures for confounding variables of problem gambling symptoms provide a very conservative way to scan the concurrent validity of the extracted thresholds of gambling behavior in addition to other personal risk factors. In a third step, thresholds of useful indicators were recalculated in the reduced training sample of only last year gamblers (45.8% of the weighted training sample), who were more involved than the entire sample of gamblers and nongamblers. The absolute threshold values extracted from both samples were compared descriptively to evaluate the impact of a plausible but arbitrary preselection.
ResultsTable 1 reflects AUC, sensitivity and specificity values of all predictor-outcome-combinations in the training set that were above the recommended AUC value of 0.7, representing useful prediction accuracy. The table shows increasing accuracy values with ascending problem levels. Both timeframes provided useful accuracy in predicting corresponding levels of problem gambling, with maximal AUC values for the composed last year predictors: maximal gambling days on favored game type (0.89–0.90) and sum of used game types (0.87–0.88). However, the composed lifetime predictors of gambling intensity in terms of losses (0.77–0.82), sum of used game types (0.81–0.84), and maximal gambling days on favored game type (0.80–0.84) also provided useful accuracy values for most combinations of predictors and outcomes. The only form-specific lifetime predictors with useful prediction accuracy remained for the number of active gambling days on gaming machines (0.72–0.77) and the number of active gambling days on poker (0.72). For the last year timeframe, only active gambling days on gaming machines (0.81–0.84) showed useful prediction accuracy. Active gambling days on all other game types did not provide adequate predictive accuracy for the different levels of problem gambling in the training data.
Accuracy and Extracted Threshold Values in the Training Sample of Last Year and Lifetime Predictors
The following Tables 2 to 5 provide information about a series of logistic regressions in the test data for each combination of an outcome, the corresponding predictors and cutoffs with useful accuracy. The regression models also include sociodemographic covariates to estimate the concurrent validity of showing gambling involvement beyond the extracted thresholds. In other words, the following regressions estimate the additional risks (in addition to inherent personal characteristics) that are posed by gambling involvement beyond the applied thresholds. In 27 of the 28 regression models, gambling intensity beyond the threshold showed larger odds ratios for problem gambling outcomes than the sociodemographic covariates. In many cases the covariates even became insignificant risk factors (only for lifetime active gambling days on poker male gender showed higher risks for gambling problems than exceeding the threshold).
Logistic Regression Models of the Test Sample for Last Year Composed Predictors
Logistic Regression Models of the Test Sample for Last Year Form-Specific Predictors
Logistic Regression Models of the Test Sample for Lifetime Composed Predictors
Logistic Regression Models of the Test Sample for Lifetime Form-Specific Predictors
Logistic Regression Models of the Test Sample for Last Year Composed Predictors
Table 2 comprises information about the eight regression models that estimate the concurrent validity of exceeding the two last year composed predictors. Maximum gambling days on the favored game type on at least 7 to at least 15 days increased the risks by the 34- to 56-fold and gambling on at least two game types (in comparison with individuals who did not exceed this threshold) increased the risk of actual problem gambling symptoms by the 38- to 65-fold.
Logistic Regression Models of the Test Sample for Last Year Composed Predictors
Table 3 includes information about the regression models that estimate the concurrent validity of exceeding the only last year form-specific predictor. Gambling on gaming machines for at least 3 days during the last year (in comparison with individuals who did not exceed this threshold) increased the risk of actual problem gambling symptoms by the 29- to 39-fold.
Logistic Regression Models of the Test Sample for Last Year Form-Specific Predictors
Table 4 provides information about the 12 regression models that estimate the concurrent validity of showing a gambling involvement beyond the three lifetime composed predictors maximal loss per year, number of used game types, and maximal active gambling days on favored game type.
Logistic Regression Models of the Test Sample for Lifetime Composed Predictors
A lifetime number of active gambling days on the favored game type of at least 11–50 days (in comparison with individuals who did not exceed this threshold) increased the risks of at least one to at least four lifetime DSM–5 criteria by the 13- to 36-fold. Maximum gambling losses in a year across the lifetime of at least 29€ to at least 100€ (in comparison with individuals who did not exceed these thresholds) increased the risks of at least one to at least four lifetime DSM–5 criteria by the 8- to 29-fold. A lifetime number of used game types of at least three to at least four types (in comparison with individuals who did not exceed these thresholds) increased the risks of at least one to at least four lifetime DSM–5 criteria by the 6- to 11-fold.
Table 5 provides information about the three regression models that estimate the concurrent validity of the only lifetime form-specific predictors of active gambling days on gaming machines and poker. Gambling at least 1–10 days on poker (in comparison with individuals who did not exceed this threshold) increased the risk of at least four lifetime DSM–5 criteria by the fourfold. Gambling at least 1–10 days on gaming machines (in comparison with individuals who did not exceed this threshold) increased the risks of at least two to at least four lifetime DSM criteria by the 5- to 7-fold.
Logistic Regression Models of the Test Sample for Lifetime Form-Specific Predictors
In a final step of analyses, useful thresholds of low-risk gambling from the entire training sample (including nongamblers and gamblers at any level of involvement) were compared with counterpart thresholds extracted from a reduced sample, only consisting of more involved last year gamblers (45.8% of the weighted training sample). Results are presented in Table 6. The AUC-values of predictive accuracy for each combination remained merely unchanged by this reduction but were slightly better in the entire sample. Moreover, the application of this preselecting filter criterion approximately doubled the absolute values of the low-risk thresholds across almost all dimensions of involvement. However, these findings did not hold for the form-specific predictors (lifetime active days on poker or gaming machines and last year active days on gaming machines). These cutoffs remained unchanged by the reduced sample.
Comparisons Between Extracted Cutoffs of the Entire Training Sample and a Preselected Sample Consisting of Only Last Year Gamblers (45.8% of the Weighted Sample)
DiscussionThe first aim of this study was to establish thresholds of probable low-risk gambling involvement for the German gambling market, following the examples of Canadian studies by synthesizing their course of analyses. In line with Currie et al. (2009), a broad range of nonexclusive criteria of gambling-related problems was applied to cover several levels of outcomes (subclinical and clinical). Furthermore, thresholds of potential problematic gambling involvement were extracted from a training sample (Currie et al., 2006) and cross-validated with confounder-analyses on a test sample (Currie et al., 2008) to check robustness against sampling variance and concurrent validity. Thereby, it was possible to establish and cross-validate several thresholds of probable problematic gambling involvement across a broad definition of gambling problems, indicators of gambling involvement and timeframes. The regression models applied to cross-validate the heuristics of probable low-risk gambling incorporated covariates like age, gender, education, migration, and unemployment status. In line with Currie et al. (2006, 2008), most effect sizes of gambling beyond the thresholds of low-risk involvement strongly exceeded the effect sizes of the sociodemographic risk factors. This relation emphasizes the outstanding role of gambling involvement as necessary cause or mediating variable in a complex net of risk posing conditions (correlative analyses from cross-sectional data do not allow further inference).
In comparison with the Canadian thresholds of probable problematic gambling involvement (Currie et al., 2006), the cutoffs on hand are lower. Probable causes may be different modes of interview (face-to-face vs. telephone), item wording (Wood & Williams, 2007), measures of problem gambling (PGSI vs. CIDI), outcomes (at least two negative consequences vs. counts of problem gambling symptoms) or preselecting filter criteria (before presenting problem gambling items or to purify the analyzed sample). Presenting evidence for the methodological impact of a filter-criterion on the absolute size of the thresholds was another aim of the study at hand. In comparison with low-risk thresholds extracted from the entire sample (nongamblers and gamblers at any level of involvement), thresholds extracted from more involved last year gamblers approximately doubled across mostly all dimensions of gambling intensity. Only low-risk thresholds for gaming machines (both timeframes) and lifetime poker were not influenced by the artificial enhancement of gambling intensity in the sample (this fact further substantiates the validity of the presented form-specific thresholds, which obviously were not influenced by this kind of filter-setting). Of course, purifying subgroups (e.g., problem gamblers vs. frequent gamblers) makes sense to some extent, but the abandonment of arbitrary filter items is also warranted to cover a more general population (e.g., first-time users included). Against the background of doubled cutoffs caused by an arbitrary filter in this study, further research is needed to optimize future approaches of extracting thresholds of low-risk gambling behavior. Further examples of important methodological influences may be adopted from research in prevalence estimation (for a summary see Meyer et al., 2015).
However, in summary (across all 28 useful combinations) last year indicators showed higher AUC values, sensitivity, and specificity than lifetime indicators. Nevertheless, some lifetime indicators also showed useful accuracy. The composed indicators “Sum of used game types,” “Maximal gambling days on favored game type,” or “Maximal loss per year across all game types” revealed higher AUC values and sensitivity than the form-specific indicators but particular form-specific indicators showed very high specificity. Consequently, it is possible to give some general recommendations in the context of preventing gambling problems for the German gambling market: (a) Last year gambling on only one game type, (b) below 7 days on the mostly used game type, and (c) below 3 days on gaming machines strongly reduced the risks of any last year DSM–5 criterion of gambling disorder. Moreover, (d) lifetime gambling on only one or two game types, (e) below 11–50 days on the mostly used game type, (f) the avoidance of gambling poker or (g) gaming machines, and (h) gambling below a self-reported maximum loss per year of 29€ strongly reduced the risks of any lifetime DSM–5 criterion of gambling disorder (in some situations it may be fruitful to apply the alternative cutoffs from Table 6). The graduated thresholds of probable low-risk gambling involvement presented in this article may help to formulate some general rules of low-risk gambling for the German gambling market, if the values will be validated successfully by results from other sources of data (particularly controlled longitudinal studies). Such empirically based rules of low-risk gambling in Germany are still up for discussion, but valuable examples do already exist for gambling in Canada (Currie et al., 2008) or for alcohol consumption in Germany (Burger et al., 2004). As long as no German rules of low-risk gambling exist, a justifiable and most cautious recommendation is lifetime abstinence because of the general risks posed by any gambling behavior. Furthermore, we advise recipients against an illusory security because gambling below the cutoffs only constitutes probabilistic information from cross-sectional associations and largely neglects individual idiosyncrasies or longitudinal impacts of complying with the thresholds. This study only represents a starting point and evidence from prospective randomized trials is indispensable to avoid negative longitudinal consequences.
Best performing predictors were both last year composed indicators: maximal gambling days on the favored game type and the number of used game types. The fact that these nonmonetary predictors outperform the monetary predictor of lifetime maximal loss per year contradicts findings of Currie et al. (2009) who revealed monetary measures as most accurate predictors (absolute spending per year; percent of gross family income spent per year). Of course, the applied monetary indicators in both studies differ in the applied timeframes (last year vs. lifetime), what might have influenced accuracy. Nevertheless, the amount of time spent involved in one or across several game types decreases the amount of available time for other activities and has proven as noticeable proxy of harmful gambling involvement (Currie & Casey, 2007). Moreover, nonmonetary indicators cancel out criticism on moderating effects of an individual’s income onto the association of monetary gambling involvement and gambling problems (Currie & Casey, 2007). Results suggest the complementary application of both indicators in future research, monetary and nonmonetary.
The last aim of this study was to explore form-specific thresholds of low-risk gambling intensity that may be reflective of probable problem gambling. On the one hand, sensitivity values of these form-specific thresholds are low, because of the fact that the entire population of problem gamblers cannot be characterized by levels of involvement in only one type of gambling. On the other hand, some form-specific thresholds showed very high accuracy, particularly in terms of specificity. For example, lifetime gambling on at least 1–10 days on gaming machines provided a specificity of 83% for at least four DSM–5 symptoms in the training sample (on poker = 95%). The specificity value of the form-specific last year threshold of gaming machines even was higher with 97%. According to the training data, addressing probable problematic individuals in gambling venues with three or more active gambling days on gaming machines in the last year will cause a false-positive decision in only 3% of the cases. High precision rates also hold for the test sample, evidenced by the large adjusted odds ratios for most thresholds in the regression-models. However, such robust associations only hold for particular game types like gaming machines and poker because of several methodological arguments: (a) Strong bivariate statistical associations of particular game types and measures of gambling problems (LaPlante, Nelson, LaBrie & Shaffer, 2011; Welte et al., 2009) provide the groundwork for a phenomenon called “spectrum bias” (Gambino, 2006, 2012). It is easier for a screener to detect severe cases and, therefore, screening instruments often show high sensitivity (one part of high accuracy) in clinical settings. Increasing accuracy with ascending severity of the predicted outcome also appeared in the analyses of Currie et al. (2009) and in the analyses at hand (see Table 1). From a methodological perspective, particular gambling venues can be seen as a clinical setting with a lot of gamblers at the upper level of a dimension of gambling-related problems and the application of thresholds of gambling intensity represents some kind of screening. This effect can also be seen in the analyses of Currie et al. (2009), in which the accuracy of the applied thresholds of gambling intensity in the subpopulations of gaming machine and casino gamblers exceeded some other values of accuracy. (b) Patrons of a particular game type are more homogeneous than the general population in terms of sociodemographic risk factors or other moderating variables. (c) The probability of a positively screened patron (after exceeding a particular threshold) to be a true problem gambler (positive predictive value) is a function of the sensitivity and specificity of a screener, but more a function of the base rate of the outcome in the context of its application (Gambino, 2006, 2012). High bivariate associations of particular game types and measures of problem gambling can be used as a kind of preselection that increases the outcome’s base rate and, therefore, improves predictive accuracy in form-specific thresholds of probable problematic gambling intensity.
Former publications raised some doubt about form-specific thresholds of gambling intensity because of poor robustness (Currie et al., 2009; Quilty et al., 2014). Evidence from the cross-validated analyses at hand confirms such issues for some types of gambling, but is contradictory for others. Furthermore, Currie et al. (2008) challenge the utility of form-specific thresholds because of the fact that most gamblers are involved in more than one type of gambling. From a preventive and person-centered perspective this argument is convincing. However, from an early detection and venue-centered perspective this argument does not hold. The extraction of form-specific thresholds from large gambling surveys provides the benefit that mostly form-specific gambling across all online platforms and offline venues is reported in an integrated manner. Hence, exceeding such threshold in one venue or online-platform is a conservative proxy measure of overall form-specific gambling involvement, let alone the entire involvement in other types of gambling, and therefore, it is a robust predictor of patrons who are potentially at risk.
Summing up theoretical and empirical evidence, extracting thresholds of probable problematic ambling involvement in particular game types from self-report surveys provides a fruitful approach in assisting gamblers, regulators, and prevention service providers, because it constitutes simple and obviously robust rules of thumb to address probable problematic gamblers on an online platform or in an offline venue. It is worth noting, however, that the stable association of slight involvement in some game types with problem gambling does not warrant any causal interpretation or evaluation of the potential risks, posed by a particular type of gambling. The low thresholds may be an indicator of high risks, already posed on low doses because of inherent characteristics of the game type. However, another plausible explanation is related to the fact that certain gambling forms go along with a selective attractiveness for vulnerable subpopulations. This study does not aim to disentangle such causal relationships. Rather, the analyses establish and validate robust statistical associations. However, a combination of the validated thresholds of involvement with current observation tools of problem gamblers in gambling venues (e.g., Delfabbro, Osborn, Nevile, Skelt, & McMillen, 2007; Hayer, Kalke, Buth, & Meyer, 2013) will probably enhance the predictive accuracy of early detection tools.
Limitations and ConclusionLike all survey data, the validity of the extracted low-risk thresholds may be biased by over- or underestimation of gambling involvement measures. Currently, there is evidence for both directions of reporting-bias (Currie & Casey, 2007) and a final position is still up for further research, even if some data are already at hand (Braverman, Tom, & Shaffer, 2014; Wood & Williams, 2007). Further research should test reliability and validity of the extracted limits with other datasets and sources (particularly with data from prospective studies and from actual gambling behavior; e.g., see: Brosowski, Meyer, & Hayer, 2012). Until the thresholds are replicated, they are only tentative heuristics and must be applied circumspectly by researchers, regulation authorities, operators, or fieldworkers. Moreover, further research has to examine the liability of the thresholds for moderating effects of gender, age, or socioeconomic status. However, strong moderating effects on the relationship between gambling intensity and problem gambling would have partially diminished the concurrent validity in the confounder models, which was not the case.
Furthermore, the cross-sectional design does not provide any evidence of causal relationships between exceeded thresholds of intensity and subsequent symptoms of problem gambling (for the Canadian cutoffs temporal precedence is confirmed; Currie et al., 2012). Nevertheless, the analyses at hand constitute a range of robust and cross-validated levels of probable low-risk gambling involvement across different indicators and timeframes, across overall gambling involvement and in particular types (poker and gaming machines). The presented heuristics may constitute a starting point to formulate evidence-based rules of low-risk gambling for national and international issues of primary prevention. Moreover, this study provides strong empirical and theoretical arguments for the application of form-specific thresholds of low-risk gambling in early detection scenarios. It also points out a fruitful roadmap of further research for gambling operators that try to assist their employees by establishing objective and robust rules-of-thumb to detect probable problematic individuals at their gambling venues. Further secondary data analyses of existing surveys of gambling behavior are obviously capable to complement current research in actual gambling behavior or observation guidelines and can provide at least one part of the answer to the issue of reliably detecting probable problematic gamblers and to reduce or avoid negative consequences of excessive gambling involvement at an early stage.
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Submitted: July 7, 2014 Revised: March 13, 2015 Accepted: March 25, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 794-804)
Accession Number: 2015-43528-008
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Record: 75- Time course of attentional bias for gambling information in problem gambling. Brevers, Damien; Cleeremans, Axel; Bechara, Antoine; Laloyaux, Cédric; Kornreich, Charles; Verbanck, Paul; Noël, Xavier; Psychology of Addictive Behaviors, Vol 25(4), Dec, 2011 pp. 675-682. Publisher: American Psychological Association; [Journal Article] Abstract: There is a wealth of evidence showing enhanced attention toward drug-related information (i.e., attentional bias) in substance abusers. However, little is known about attentional bias in deregulated behaviors without substance use such as abnormal gambling. This study examined whether problem gamblers (PrG, as assessed through self-reported gambling-related craving and gambling dependence severity) exhibit attentional bias for gambling-related cues. Forty PrG and 35 control participants performed a change detection task using the flicker paradigm, in which two images differing in only one aspect are repeatedly flashed on the screen until the participant is able to report the changing item. In our study, the changing item was either neutral or related to gambling. Eye movements were recorded, which made it possible to measure both initial orienting of attention as well as its maintenance on gambling information. Direct (eye-movements) and indirect (change in detection latency) measures of attention in individuals with problematic gambling behaviors suggested the occurrence of both engagement and of maintenance attentional biases toward gambling-related visual cues. Compared to nonproblematic gamblers, PrG exhibited (a) faster reaction times to gambling-cues as compared to neutral cues, (b) higher percentage of initial saccades directed toward gambling pictures, and (c) an increased fixation duration and fixation count on gambling pictures. In the PrG group, measures of gambling-related attentional bias were not associated with craving for gambling and gambling dependence severity. Theoretical and clinical implications of these results are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Time Course of Attentional Bias for Gambling Information in Problem Gambling
By: Damien Brevers
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium;
Consciousness, Cognition & Computation Group, Université Libre de Bruxelles, Belgium;
Axel Cleeremans
Consciousness, Cognition & Computation Group, Université Libre de Bruxelles, Belgium
Antoine Bechara
Psychiatry Department and Faculty of Management, McGill University;
Department of Psychology, University of Southern California
Cédric Laloyaux
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Charles Kornreich
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Paul Verbanck
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Xavier Noël
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Acknowledgement: This research was supported by the Belgium National Lottery and the National Fund for Scientific Research, Belgium. We thank Michael Baker, Table Games Manager for the VIAGE casino complex (Brussels, Belgium), for his help in recruiting gamblers participants.
The main goal of this study is to explore the time course of the deployment of attention in pathological gamblers as they process visual stimuli that are or are not related to their addiction.
Pathological gambling (PG), similar to other addictions, can be operationally defined as the continuation of maladaptive choices despite the occurrence of aversive consequences (e.g., relationship, job; APA). PG afflicts about 1.6% of the general population (Inserm, 2008). With growing availability of gambling opportunities, prevalence of PG is rising and beginning to pose a serious public health problem (Inserm, 2008).
Numerous studies have shown that addiction-related cues are processed more efficiently by addicted individuals, thus further reinforcing subsequent maladaptive cognition and behaviors (for a review see, Field & Cox, 2008; Field, Munafò, & Franken, 2009). According to the incentive-sensitization theory (Robinson & Berridge, 1993; Robinson & Berridge, 2003), compulsive gambling (as other states of addiction) might be caused primarily by repeated exposure to gambling-related stimuli that would induce gambling sensitization in the brain's meso-limbic and meso-cortical dopamine systems that attribute incentive salience to reward-associated stimuli. In other terms, pathological motivation could arise from sensitization of brain circuits that mediate Pavlovian conditioned incentive motivational processes. Therefore, this sensitization might occur even in the absence of drug actions, such as in abnormal gambling. Once rendered hypersensitive, these systems generate pathological incentive motivation (i.e., wanting) for addictive behaviors. During wanting, incentive salience, which is a type of incentive motivation, plays a role in promoting approach toward, and consumption of, rewards. Wanting has distinct psychological and neurobiological features from liking. In this context, incentive sensitization could produce an attentional bias toward processing drug-associated stimuli and pathological motivation for drugs (compulsive wanting; Robinson & Berridge, 1993, 2003).
Recent theoretical models of addiction (e.g., Baker, Morse, & Sherman, 1987; Field & Cox, 2008; Franken, 2003; Kavanagh, Andrade, & May, 2005; Robinson & Berridge, 1993; Ryan, 2002) suggest that attentional biases for substance-related cues experienced by substance users could modulate aspects of subjective experience (e.g., craving) and influence addictive behaviors. A recent meta-analysis by Field, Munafò, and Franken (2009) indicated a modest but statistically significant positive correlation between subjective craving, as assessed with self reported measures, and attentional bias. Moreover, this study highlighted a marginally significant effect for a larger association between craving and attentional bias when measures of the maintenance of attention from substance-related cues were compared with measures of the initial orienting response of attention. One possible explanation for this difference is that the attentional maintenance measure better reflects the specific attentional processes that are influenced by incentive mechanisms, namely, a bias to hold attention on motivationally salient cues (LaBerge, 1995). These insights led us to investigate the relationship between attention biases and craving in problem gamblers.
Although numerous studies have focused on the attentional biases in individuals who abuse substances such as alcohol, drugs and/or tobacco (for a recent review of attentional biases in addicts see Field & Cox, 2008 and Field et al., 2009), little is known about attentional bias in addictive disorders that do not necessarily involve the ingestion of exogenous substances, namely pathological gambling. For instance, using a modified Stroop paradigm, participants with compulsive gambling took longer to name the color of words relating to gambling compared to healthy controls or to low problem gamblers (Boyer & Dickerson, 2003; Molde et al., 2010). However, performance on the modified Stroop paradigm does not allow investigation of more specific attentional processes, such as the initial orienting component, which is typically followed by attentional capture or repulsion (Jones, Bruce, Livingstone, & Reed, 2006). Hence the main goal of this study was to investigate the effects of gambling on these specific processes of attention.
To shed further light on the nature of gambling-related attentional bias, we used a change detection task called “the flicker paradigm” (Rensink, O'Regan, & Clark, 1997; Simons & Rensink, 2005), which has often been used to demonstrate “change blindness”. This task consists of consecutive and repeated presentations of two identical visual scenes separated by a mask (typically a gray screen), that differ in only one element. The presentation of the visual scenes continues until the change is detected. With normal individuals, the number of presentations necessary for the change to be detected is much higher than what would be expected based on a direct comparison of the two alternating pictures—hence the expression “change blindness,” for participants are surprisingly found to be unable to detect changes that are typically obvious under normal viewing conditions. The number of repetitions required for the change to be detected thus constitutes the main dependent measure in this paradigm, and it has been shown to be influenced by specific conditions or with specific populations. For instance, some studies have reported faster change detection latency by problematic heavy drinkers for addiction-related changes compared to neutral ones (Jones, Jones, Smith, & Copley, 2003; Jones, Bruce, Livingstone, & Reed, 2006).
However, a main limitation of classic behavioral paradigms (such as the flicker paradigm but also modified Stroop and visual probe tasks) is that they do not make it possible to explore the time course of the allocation of attention. Tracking eye movements, by contrast, in addition to being ecologically valid, importantly enables the investigation of attentional biases not only at stimulus offset but also during the entire duration of the stimulus presentation (Schoenmakers, Wiers, & Field, 2008). Attentional biases as revealed by the pattern of eye movements in response to a visual stimulus (i.e., prolonged maintenance of gaze, or a higher proportion of initial eye movements directed toward addiction-related vs. neutral cues) has so far been demonstrated only in individuals addicted to psychoactive substances such as tobacco, cannabis, alcohol, and drugs (for a review see, Field et al., 2009).
In summary, we aimed to investigate the nature of gambling-related attentional bias in a group of problem gamblers (PrG) by using a flicker paradigm for induced change blindness with direct (i.e., eye movements recording) and indirect (i.e., change detection latency) measures of attentional processes. We test three primary hypotheses: compared to normal control (CONT), PrG would (a) detect a gambling-related change more rapidly than a neutral change; (b) direct their initial eye movement toward gambling-related cues, indicating facilitated attentional engagement toward gambling stimuli; (c1) show prolonged maintenance of gaze toward gambling-related elements compared to neutral stimuli, and (c2) would exhibit a higher proportion of eye movements toward gambling-related elements, indicating attentional maintenance on gambling cues. In addition, we expect to find an association between gambling self-reported craving and maintenance of attention toward gambling cues.
Method Participants
Two groups participated in the study: (a) a control group (CONT; n = 35) and (b) a problem gamblers (PrG) group (n = 40). All subjects were adults (>18 years old) and provided informed consent that was approved by the appropriate human subject committee at the Brugmann University Hospital. The demographic data on the two groups are presented in Table 1.
Demographical Data and Standard Deviations For Pathological Gambling (PG), Problem Gambling (PrG) and Normal Control (CONT) Groups
Recruitment and Screening Methods
Gambling dependence severity was assessed with the South Oaks Gambling screen (SOGS; Lesieur & Blume, 1987). Scores on the SOGS can vary between 0 and 20. An example of an item is: “Have you ever borrowed from someone and not paid them back as a result of your gambling?”
All PrG (n = 40) scored ≥ 3 (max = 8) on the SOGS, indicative of problem gambling, and 13 participants (32.5%) met the more stringent criteria for probable pathological gambling (SOGS ≥ 5). On the basis of Lawrence, Luty, Bogdan, Sahakian, and Clark (2009), we will refer to this combined group henceforth as PrG. Distribution of SOGS scores in the PrG group is presented in Table 1. CONT were recruited by word of mouth from the employees at the psychiatric unit of the Brugmann University Hospital. To avoid biases, resulting from inside knowledge of how these tasks operate, Psychiatrists, Psychologists and other personnel having had psychological training were excluded from participation. On the SOGS, only six CONT (17%) reported playing the numbers or betting on lotteries occasionally (i.e., less than once a week). All remaining control participants reported not gambling at all.
Current Clinical Status
Current clinical status of depression and anxiety was rated with French versions of the Beck Depression Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1967) and the Spielberger State–Trait Anxiety Inventory (STAI; Spielberger, 1993), respectively. The number of cigarettes per day was also included on the basis of previous studies (e.g., Heishman, 1998) that highlighted an effect of nicotine dependence on cognitive processing (e.g., sustained attention). We excluded any control subject who met an Axis I psychiatric diagnosis assessed by the Structured Clinical Interview for DSM–IV (First, Spitzer, Gibbon, & Williams, 2002), who had experienced a drug use disorder during the year before enrollment in the study, or who had consumed more than 54g/day of alcohol for longer than one month. On the basis of the results of their medical history and physical examination, they were judged to be medically healthy. All participants were asked to avoid the use of drugs, including narcotic pain medication, for the five days prior to testing and to avoid alcohol consumption for the preceding 24 hr.
Self-Report Measure of Gambling-Related Craving
We used the Gambling Craving Scale (GACS; Young & Wohl, 2009) to assess subjective craving toward gambling in PrG. The GACS contains three factors: Anticipation (e.g., “Gambling would be fun right now”), Desire (e.g., “I crave gambling right now”) and Relief (e.g., “If I were gambling now, I could think more clearly”). There are nine items (three items for each of the three factors) assessed on a 7-point scale. For this study, the GACS was translated into French. Back translation method was used. For the present sample, using Cronbach's alpha, the internal consistency reliability was .78, .84, .89 for the factors Anticipation, Desire, and Relief, respectively.
Paradigm and Design
The original stimulus (OS) was presented for 250ms, followed by the mask (M) for 80 ms, then the changed stimulus (CS) for 250ms. The OS-M-CS-M series was continuously presented until change detection. Based upon previous research with the flicker paradigm (Jones, Jones, Blundell, & Bruce, 2002; Jones et al., 2003, 2006), participants performed only one single-flicker task (in the current case, to detect either the gambling related or the neutral change). The dependent variable was change-detection latency, direction of first eye movement, proportion of eye fixation count and length.
We controlled for the possibility that information from the left hemispace might be processed more readily than information from the right hemispace in normal individuals (i.e., pseudoneglect; e.g., Nicholls, Orr, Okuba, & Loftus, 2006). Therefore, all participants were randomly assigned to one of four flicker conditions, leading to a 2 (CONT vs. PrG group) × 2 (gambling-related vs. neutral change) × 2 (bilateral organization of the stimulus; gambling stimuli on the left and neutral on the right, GN, vs. neutral left and gambling right, NG) between-subjects design.
Apparatus and Stimuli
The OS consisted of a matrix of 18 full color photographs depicting nine gambling related and nine neutral objects on each side (see Figure 1). The nine pairs of gambling and neutral objects were selected so that their physical properties (e.g., color, height, width, shape) were similar. The two sets of nine photographs were arranged in two 3 × 3 matrices set in a 3 × 6 landscape matrix, with items of each matched pair occupying corresponding positions across their respective matrices. The CS with the gambling related change was identical to the OS except that the object at the center of the gambling matrix was substituted (see Figure 1b).
Figure 1. The original stimuli (OS) and changed stimuli (CS) used in the flicker paradigm for induced change blindness. Panel 1a. Two OS (gambling-right, neutral-left, NG, and neutral-right, gambling-left, GN); Panel 1b. Two CS, CS-gambling-related-change (gambling-right, neutral-left, NG, and neutral-right, gambling-left, GN); Panel 1c. Two CS, CS-neutral-change (gambling-right, neutral-left, NG, and neutral-right, gambling-left, GN).
There was a second CS with a corresponding neutral substitution (Figure 1c). The two different CSs with their common OS represented the two levels of Factor 2 (nature of change). Finally, bilateral reversals of each of the OS and the two CSs were made for the two levels of Factor 3 (i.e., GN and NG). The single mask comprised rows of uppercase, 20-point Xs in Times New Roman font.
This task consists of consecutive and repeated presentations of two identical visual scenes separated by a mask (typically a gray screen), that differ in only one element. The presentation of the visual scenes continues until the change is detected.
A TOBII ×120 eye tracker was used to measure participant's eye movements. The TOBII ×120 records the X and Y coordinates of participant's eye position at 60 Hz by using corneal reflection techniques. Calibration procedures were run using Clearview software (TOBII Technology, Sweden) which allows an optimal accuracy of 0.5 degrees. Stimulus presentation and data output for the flicker task were programmed in E-Prime version 2.0 professional and appeared on a 17 in. CRT-monitor with a refresh rate of 85 Hz.
The eye tracking software and measures were run and recorded on an Intel Xenon based PC, which was linked to an Intel Core 2 based laptop through a local area network. E-Prime software was used on the Intel Core 2 based laptop, which also recorded the change-detection latency measure.
Procedure
Testing took place individually and in a quiet room, located at the Medical Psychology Laboratory of the Brugmann Hospital. Participants were invited to first complete the STAI-State (Spielberger, 1993). Participants were seated 60 cm in front of the TOBII monitor. The experimenter manipulated the monitor until the cameras detected participants' corneal reflection. Participants were then shown a series of looming balls that appeared in a 5-point calibration sequence. Calibration accuracy was checked and repeated if necessary. Before performing the flicker task, participants were shown a preview of the flicker paradigm for induced change blindness, but with unrelated objects than those used in the following flicker paradigm and without the difference between OS and CS. This was made to accustom participants to the fast stimuli's appearance rate. Participants then performed the flicker task with a gambling or neutral change. They were asked to watch a series of nearly identical pictures “flicked back and forth” on the screen and to detect the difference between them as quickly as possible. Participants had to indicate that they had detected a change by quickly saying “STOP” aloud, at which moment the experimenter pushed a dedicated button on a wireless gamepad to time-stamp the moment of change detection. Immediately after the flicker task, PrG participants were required to fill out the GACS.
Data Analysis
Change-detection latency
Change-detection latency was the total number of combined OS-M-CS-M presentations until change detection. We performed a univariate analysis of variance (ANOVA) with group (CONT vs. PrG), type of change to be detected (gambling−related vs. neutral) and bilateral organization of the stimulus (gambling stimuli on the left and neutral on the right, GN vs. neutral left and gambling right, NG) as between-subjects factors, and change detection latency as dependent variable.
Direction of first eye movement
The first eye movement was defined as the first fixation lasting at least 100 ms in the region of either the gambling or neutral stimulus, at least 100 ms after the first OS onset. This enabled us to calculate the percentage of initial eye movements that were directed at gambling-related versus control pictures during the task. To examine whether participants showed a bias in the first eye movement direction during the flicker task, the percentage of initial eye movements toward gambling pictures was compared with 50% (which indicates no bias).
Proportion of fixation count
Proportion of fixation count was the total number of eye-fixation directed toward gambling or neutral stimuli until change detection divided by the total amount of eye-fixations. Fixation count was analyzed using ANOVA with repeated measures, with group, type of change to be detected and bilateral organization of the stimulus as between-subjects factors; with type of stimulus (gambling, neutral) as a within subjects factor; and proportion of fixation count, as the dependent measure.
Proportion of fixation length
Proportion of fixation length was the total time (ms) of eye-fixation directed toward gambling or neutral stimuli until change detection divided by the total length of eye-fixation. Fixation length was analyzed using ANOVA with repeated measurements, with group, type of change to be detected and bilateral organization of the stimulus as between-subjects factors; type of stimulus (gambling, neutral) as a within subjects factor; and fixation length, as the dependent measure.
Association between gambling related attentional bias, self-reported gambling-related craving and gambling dependence severity in PrG
Correlation analyses were conducted between the gambling-related attentional bias measures, total score of the GACS, scores of the three factors of the GACS and score on the SOGS (n = 40). A univariate ANOVA was also conducted with direction of first eye movement (neutral vs. gambling), as between-subjects factors, and total score of the GACS, scores of the three factors of the GACS and score on the SOGS score as dependent variable.
Results Demographics and Current Clinical Status
A description of demographic variables, scores on the South Oaks Gambling Screen (SOGS), Beck Depression Inventory (BDI), the Trait and State version of the State–Trait Anxiety Inventory (STAI) and the average number of cigarettes smoked per day is presented in Table 1. Chi-square analyses revealed no differences in the number of male and female participants. Depression was higher in PrG than in CONT, t(73) = 2.11, p < .05. State and trait anxiety was higher in the PrG group in comparison with the CONT group, t(73) = −2.16, p < .05; t(73) = −2.01, p < .05, respectively. The average number of cigarettes smoked per day was higher in PrG than in CONT, t(73) = 2.81, p < .01. No other group differences were present. Because our sample of PrG included individuals who met the more stringent criteria for probable pathological gambling, the effect of gambling severity was controlled for the PrG group. In the absence of effect covariate effect of depression, trait–state anxiety, and number of cigarettes smoked per day on group comparisons, we performed ANOVAs.
Change Detection Latency
All participants detected all changes correctly. The ANOVA showed no main effects of Group, Type of Change, or Stimulus Orientation (all p > .05). There was no interaction except for the following one, which supported the gambling-related attentional bias hypothesis in problem gamblers. An interaction between groups and type of change was found, F(1, 67) = 10.57, p < .01, η2 = .13. This analysis showed that PrG' change-detection latency for the gambling-related change was smaller than for the neutral change. Control participants' change-detection latency for the gambling-related change and for the neutral change, however, were not different (see Figure 2).
Figure 2. Latency to change-detection for CONT and PrG with gambling-related and neutral changes.
Direction of First Eye Movement
The percentage of first eye movements toward gambling pictures was significantly greater than 50% in the PrG group but not in the CONT group, t(39) = 2.73, p < .01 and t(34) = .12, ns, respectively. Also, a t test revealed that the first eye movement percentages toward gambling pictures differed significantly between groups, t(74) = 4.71, p < .05.
Proportion of Fixation Count
There were interactions between type of change and fixation count, F(1, 67) = 16.68, p < .001, η2 = .19, and between group and fixation count, F(1, 67) = 6.04, p < .05, η2 = .08. Analyses revealed that participants fixation on change-related stimuli occurred more frequently (M = .58, SD = .15) compared to stimuli not linked to the change (M = .42, SD = .15). The other interaction effect revealed that PrG group, but not CONT, fixated on gambling-related stimuli more frequently compared to neutral stimuli. Results of the group × type of stimulus interaction are presented in Figure 3.
Figure 3. Proportion of fixation count for CONT and PrG with gambling-related and neutral stimuli.
Fixation Length
Analyses revealed a type of change × fixation length interaction, F(1, 73) = 13.31, p < .001, η2 = .17, and a group × type of stimulus interaction, F(1, 73) = 9.78, p < .001, η2 = .13. Analyses revealed that participants fixated longer change-related stimuli (M = .57, SD = .17) compared to stimuli not linked to the change (M = .43, SD = .17). For the other interaction, the analyses revealed that PrG group fixated much longer gambling-related stimuli compared to neutral stimuli (see Figure 4).
Figure 4. Proportion of fixation length for CONT and PrG with gambling-related and neutral stimuli.
Association Between Gambling Related Attentional Bias, Self-Reported Gambling-Related Craving and Gambling Dependence Severity In PrG
Correlation analyses (n = 40) revealed that there was no significant correlation between the gambling-related attentional bias measures, the total score of the GACS, scores on the three factors of the GACS and score on the SOGS (see Table 2). There was also no significant difference between the direction of first fixation on both GACS and SOGS scores (F < 1).
Correlation (n = 40) Between Gambling Related Attentional Bias, Self-Reported Gambling-Related Craving and Gambling Dependence Severity In PrG
DiscussionThe main findings of the present research could be summarized as follows: comparison of the PrG and the CONT showed that PrG are faster in detecting gambling-related changes in the flicker paradigm, exhibit more gaze fixation counts and longer fixation lengths toward gambling-related stimuli. In addition, unlike CONT, the percentage of first eye movements toward gambling cues was higher and significantly above chance level for the PrG group.
As hypothesized, behavioral data (indirect measure of attention) recorded during the flicker paradigm showed that, in comparison with CONT, PrG, all of whom met criteria for problem gambling based on their scores on the South Oaks Gambling Screen (SOGS), are faster to detect gambling-related change. This result suggests that PrG's attention is captured by gambling related cues, that is, attentional bias. This finding is in line with studies showing that on a modified version of the Stroop task, PrG's take more time to name the color of the words related to gambling practices than neutral one(s) (Boyer & Dickerson, 2003; Molde et al., 2010).
We then set out to ascertain whether this attention bias was due to engagement or/and maintenance of attention. To do so, participants' eye movements were monitored using eye-tracking technology (direct measure of attention). Compared to control participants, PrG directed their first eye movements more frequently toward gambling-related than toward neutral stimuli (bias of attentional engagement), exhibit more gaze fixation counts on gambling stimuli and spent more time looking at gambling-related (bias of attentional maintenance) than control stimuli. This pattern of eye-movements suggests that both initial engagement and maintenance of attention are parts of the problem that drive gambling cognition and behavior.
Contrary to our hypothesis, we found no significant correlation between the maintenance of attention and craving scores assessed with the Gambling Craving Scale (GACS). An explanation for the absence of a relationship between attentional bias and craving is that, like substance addictions, it may occur automatically and habitually in the absence of any conscious subjective experience of craving (Tiffany, 1990). As an alternative explanation, the absence of relationship between craving and attentional bias might be accounted for by a low subjective craving in PrG at the time of assessment. Indeed, scores on the GACS' subscales revealed that PrG experienced an intention to gamble that was anticipated to be fun and enjoyable (the Anticipation scale) rather than a strong, urgent desire to gamble (the Desire scale) and an expectation that gambling would provide relief from negative affect (the Relief scale). Moreover, there was also no association between gambling-related attentional bias and gambling dependence severity. This was probably due to the relatively small variation of SOGS' scores between PrG participants.
Findings related to the presence of attentional bias in PrG are consistent with the incentive-sensitization theory (Robinson & Berridge, 1993, 2003). This model proposes that attentional and approach biases for addiction-related stimuli are an indication of incentive processes, and that incentive sensitization mechanisms play an important role in the development and the maintenance of an addiction state. The presence of attentional bias in PrG as well as in individuals addicted to substance (alcohol, cannabis, tobacco, heroin, and cocaine; for a review see Field, Munafo, & Franken, 2009) suggests that this shared component may lead to poor self-regulation. Most importantly, it raises the possibility that gambling-related attentional bias might be a treatment target (van Holst, van den Brink, Veltman, & Goudriaan, 2009). Indeed, decreasing attentional biases with the help of behavioral therapy and modification paradigms may result in increasing likelihood to select alternative behaviors to have fun (or to feel less anxiety).
A limitation of this paper is that we cannot isolate the “problem gambling” component per se since problem gamblers have been compared to nongamblers instead of healthy nonproblem gamblers. This problem limits the generalizability of our results. Therefore, it is certainly important to extend this research to a larger sample of gamblers which has both extreme ends of the spectrum of gambling dependence well represented, including healthy nonproblem gamblers (e.g., usual lottery players) as well as pathological gamblers who attempt to stop gambling. Furthermore, on the basis of Tiffany (1990), gamblers who want to stop gambling may experience extreme deprivation conditions that would elicit strong incentive effects (and associated intense craving) toward gambling-related stimuli, such that attentional bias for gambling cues may rise to ceiling levels. Finally, even if we did not seek in this experiment to investigate the relationship between the intensity of craving and attentional biases in control, the Gambling Craving Scale could also be administrated to these subjects in further studies. Such research might clarify the precise nature of the relationships between state and trait gambling-related variables (e.g., craving, gambling dependence severity) and the cognitive and behavioral indications of incentive salience processes (e.g., attentional biases), given that these incentive mechanisms are proposed to play a key role in maintaining addictive behaviors and in increasing the risk of relapse following quit attempts.
In summary, direct and indirect measures of attention in individuals with problematic gambling behaviors emphasized the presence of both attentional engagement and maintenance biases toward gambling-related pictorial cues during a flicker paradigm for induced change blindness. These attentional biases correspond well to those seen in substance addiction, including alcohol, tobacco, cannabis, heroin, and cocaine. This research is consistent with models of addiction which suggest that addiction-related cues acquire incentive-motivational properties.
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Submitted: September 7, 2010 Revised: March 22, 2011 Accepted: April 20, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (4), Dec, 2011 pp. 675-682)
Accession Number: 2011-12261-001
Digital Object Identifier: 10.1037/a0024201
Record: 76- Underage drinking among young adolescent girls: The role of family processes. Fang, Lin; Schinke, Steven P.; Cole, Kristin C.; Psychology of Addictive Behaviors, Vol 23(4), Dec, 2009 pp. 708-714. Publisher: American Psychological Association; [Journal Article] Abstract: Guided by family interaction theory, this study examined the influences of psychological, peer, and familial processes on alcohol use among young adolescent girls and assessed the contributions of familial factors. An ethnically diverse sample of 1,187 pairs of girls (M age = 12.83 years), and their mothers completed surveys online. Questionnaires assessed girls’ lifetime and recent alcohol use, as well as girls’ demographic, psychological, peer, and family characteristics. Hierarchical logistic regression models showed that although girls’ drinking was associated with a number of psychological and peer factors, the contributions of family domain variables to girls’ drinking were above and beyond that of psychological and peer factors. The interaction analyses further highlighted that having family rules, high family involvement, and greater family communication may offset risks in psychological and peer domains. Study findings underscore the multifaceted etiology of drinking among young adolescent girls and assert the crucial roles of familial processes. Prevention programs should be integrative, target processes at multiple domains, and include work with parents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Underage Drinking Among Young Adolescent Girls: The Role of Family Processes
By: Lin Fang
Factor-Inwentash Faculty of Social Work, University of Toronto, Ontario, Canada;
Social Policy Center, National Taiwan University, Taipei, Taiwan;
Steven P. Schinke
School of Social Work, Columbia University, New York, New York
Kristin C. Cole
School of Social Work, Columbia University, New York, New York
Acknowledgement: This work was supported by National Institute on Drug Abuse grant R01 DA17721. We thank Kevin Barnes-Ceeney, Hyeouk C. Hahm, and Tahany M. Gadalla for their helpful comments.
Underage drinking among girls is a growing problem. Not only are girls closing the gender gap in the prevalence of their alcohol use, but among younger girls in particular, they are reporting higher rates of use than boys (Johnston, O’Malley, Bachman, & Schulenberg, 2009). Among the explanations offered for girls’ underage drinking is family interaction theory (Brook, Brook, Gordon, Whiteman, & Cohen, 1990). This theory posits that adolescents’ alcohol use results from psychological, peer, and family influences, and suggests that strong parent–child involvement and communication and high levels of parental monitoring can protect girls.
Family interaction theory is especially salient for adolescent girls. Whereas alcohol use among boys is usually explained by personal beliefs (Fisher, Miles, Austin, Camargo, & Colditz, 2007; Yeh, Chiang, & Huang, 2006), family relationships (Yeh et al., 2006) and involvement (Fisher et al., 2007) are better predictors of girls’ alcohol use. Moreover, despite increasing knowledge of predictors associated with underage drinking, the relative contributions of familial variables remain unclear. Although some studies suggest that psychological factors such as depression (Silberg, Rutter, D’Onofrio, & Eaves, 2003), body esteem (National Center on Addiction and Substance Abuse, 2003), and self-efficacy (Kumpulainen & Roine, 2002) as well as peer influence (Farrell & White, 1998; Simons-Morton, Haynie, Crump, Eitel, & Saylor, 2001) are strongly associated with adolescent girls’ drinking, other findings support familial factors as stronger predictors of girls’ drinking (Cleveland, Feinberg, Bontempo, & Greenberg, 2008).
Informed by family interaction theory, this study investigated how demographic, psychological, peer, and family factors explain girls’ alcohol use. We hypothesized that: (1) higher levels of depression, less body esteem, lower self-efficacy, and greater levels of perceived peer alcohol use would be related to girls’ drinking; (2) after controlling for the contributions of psychological and peer variables, familial factors, namely maternal drinking, parental monitoring, family rules against girls’ alcohol use, parental involvement, and mother–daughter communication, would be associated with girls’ alcohol use; (3) familial domain variables would explain girls’ drinking over and above that accounted for by psychological and peer domain variables; and (4) familial domain variables would modify the effects of psychological and peer factors on girls’ alcohol use.
Method Procedures
The study involved a cross-sectional, Web-based survey of mother–daughter dyads. Study participants were recruited between September 2006 and December 2007 through advertisements in newspapers, public transportation, and on radio stations, and postings on the Web site craigslist.org. To be eligible, girls needed to be aged between 10 and 14 years, have private computer access, gain their mothers’ active participation, and live in the metropolitan New York area. Informed assent and consent forms were sent to eligible girls and their mothers by mail. Of the 1,911 mother–daughter pairs contacted, 20.4% (n = 390) did not respond, 14.6% (n = 279) were no longer interested, 2% (n = 38) were deemed ineligible for the study, and 63% (n = 1,204) agreed to participate and consented. Our consent rate was higher than the average rate (34%) garnered by other Web surveys (Shih & Fan, 2008). Once assent and consent were established, girls and mothers completed online measures. Participants reported before and after the survey whether they were taking the survey alone, and could not begin the online measures until they confirmed their privacy. Less than 2% (n = 17) reported that other people were present while they completed the survey. Responses for these 17 dyads were excluded from data analyses. The average time required to complete the survey for girls was roughly 35 min, and for mothers roughly 20 min. Girls and mothers received $25 each for completing the survey. The study protocol was approved by Columbia University Morningside Campus Institutional Review Board.
Participants
The sample was 1,187 pairs of adolescent girls (M age = 12.83 years; SD = 1.03; 34.9% were Black, 26.2% were White, 21.1% were Latino, 8.5% were Asian, and 9.3% were mixed race) and their mothers (M age = 40.28 years; SD = 6.66). Less than one-half of the girls (42.6%) lived in a single-parent household. Most girls reported receiving B’s (42.3%) or A’s (38.9%) at school. About two-fifths of mothers (42.1%) had some college education or an associate degree.
Measures
Girls’ drinking behavior
Girls reported whether they had ever had a whole drink of an alcoholic beverage (i.e., beer, wine, malt liquor, wine coolers, sweet alcoholic drinks, mixed drinks, and hard liquor) in their lifetime, and during the past 30 days (0 = have never drunk; 1 = have drunk).
Demographic and background variables
Girls reported their age, ethnic-racial backgrounds, and estimated average academic grades (1 = D’s and below to 4 = A’s). Mothers provided information on their age, levels of education (1 = less than high school; 2 = high school degree; 3 = some college or associate degree; 4 = undergraduate degree; 5 = graduate degree), and family composition (0 = single-parent household; 1 = two-parent household).
Depression
Girls rated their depressed mood, hedonic capacity, vegetative functions, and interpersonal behaviors on the short version of the Children’s Depression Inventory (CDI; Kovacs, 1992). The scale had 10 items. Possible responses ranged from 0 to 2. The scores were averaged, with higher scores indicating more definite depressive symptoms. Alpha was .89 for the girls in our study.
Body esteem
On a 5-item physical appearance subscale of the Self-Perception Profile for Adolescents (Harter, 1988), girls specified the degree to which they were happy with the way they looked and with their height and weight. Possible averaged scores ranged from 1 to 5, where higher scores reflected greater levels of body esteem. Alpha was .86 in this study.
Self-efficacy
Girls indicated their levels of self-efficacy by reporting their confidence in abstaining from alcohol use in situations associated with alcohol use on 5 items derived from the Alcohol Abstinence Self-Efficacy Scale (DiClemente, Carbonari, Montgomery, & Hughes, 1994). Response choices ranged from 1 to 4, with higher averaged scores representing greater self-efficacy. Alpha was .85 for the girls in our study.
Perceived peer alcohol use
Girls estimated how many of their closest friends drank and how many of them got drunk on a 5-point scale (Johnston, O’Malley, & Bachman, 2001). Possible responses ranged from 0 to 4. Alpha was .85 in our study.
Maternal drinking
Mothers reported whether they drank during the past 30 days, where never drank was coded as 0, and ever drank was coded as 1.
Parental monitoring
On the Parenting Practices Questionnaire (Gorman-Smith et al., 1996), mothers indicated their parental monitoring on a 5-item measure, and reported their awareness of daughter’s whereabouts, activities, friends, and peer activities. Response options ranged from 1 to 5. Scores were averaged, with higher scores indicating greater parental monitoring. Alpha for the mothers in our study was .82.
Family rules against alcohol use
Responding to a 3-item scale from Strengthening Families Program evaluations (Spoth, Redmond, & Shin, 1998), mothers assessed the extent to which they communicated specific rules about their child’s use of alcohol and the consequences for not following those rules. Possible scores ranged from 1 to 5, with higher averaged scores signifying more family rules against alcohol use. Alpha was .84 for the mothers in our study.
Parental involvement
Mothers reported how often they checked their daughter’s homework and whether the family ate dinner and lunch together on a 3-item scale (Griffin, Botvin, Scheier, Diaz, & Miller, 2000). Responses ranged from 0 to 4. Higher averaged scores signified greater family involvement. Alpha for was .82 for the mothers in our study.
Mother–daughter communication
Girls rated the communication with their mothers when faced with problems and conflicts on the adapted Family Problem Solving Communication Index (McCubbin, Thompson, & McCubbin, 1996). Reponses on this 5-item scale ranged from 1 to 5, where higher averaged scores showed better mother–daughter communication. In our study, the alpha was .81.
Statistical Analysis
Hierarchical logistic regression analysis was conducted for each of two dependent variables—girls’ lifetime and recent alcohol use. The hierarchical sequence of psychosocial domains entered in the models was guided by study hypotheses as informed by family interaction theory. In each set of analyses, we entered background variables in Block 1 of the regression equation, and psychological factors including girls’ depression, body esteem, and self-efficacy in Block 2. Because we were interested in assessing the effects of family processes after accounting for girls’ psychological states and peer influence, we entered the perceived peer use variable in Block 3. Familial factors—maternal drinking, parental monitoring, family rules against alcohol use, mother–daughter communication, and parental involvement—were added in Block 4 to determine whether familial factors predicted alcohol use beyond all other variables entered earlier. Finally, we tested an interaction model that examined whether familial factors moderated the association of psychological factors and peer factors with girls’ drinking. We developed separate models for each of the interaction terms (five familial variables × four psychological and peer factors). To reduce multicollinearity and facilitate the interpretation of the interaction terms, centered variables were used to create product terms for each potential interaction (Aiken & West, 1991). To reduce Type I error, all confidence intervals were adjusted for multiple comparisons in the interaction analyses (Jaccard, 2001). For each model, demographic, psychological, peer and family variables, and the corresponding product term were entered as predictors. Variables within each block were entered simultaneously. All analyses were conducted in SPSS 16.0 (SPSS Inc., 2007).
ResultsAcross the sample, 39.7% (n = 471) of girls reported ever drinking one alcoholic beverage and 9.8% (n = 116) had at least one whole drink recently (in the past 30 days). Girls’ drinking rates for the current study were higher than the national average of 23.1% (lifetime) and 7.7% (past 30 days) among girls aged 12 to 14 years (Pemberton, Colliver, Robbins, & Gfroerer, 2008). Table 1 shows the group differences between girls who drank and those who did not. Older age, poorer academic performance, greater levels of depression, higher perceived peer alcohol use, and higher levels of maternal drinking were observed in the group of girls who ever drank and drank recently, whereas higher levels of body esteem, self-efficacy, parental monitoring, family rules against alcohol use, and family involvement were found in the group of girls who did not drink. Girls’ race, mothers’ education, and family composition did not differ by girls’ drinking behavior.
Summary of Major Study Variables and Group Differences by Girls’ Lifetime and Recent (Past 30 Days) Alcohol Use (N = 1,187)
Hierarchical Logistic Regression Analyses
Separately for lifetime (Table 2) and recent alcohol use (Table 3), hierarchical logistic regression analyses tested the hypothesized relationships between independent variables and girls’ drinking, and examined the relative contributions of familial process variables. Independent variables significantly related to girls’ drinking on a bivariate level were entered in the regression models. Given the girls’ young age, we examined lifetime and recent alcohol use. Whereas the lifetime drinking model provides an understanding of why the girls began to drink, the recent drinking model yields information about correlates associated with girls’ current alcohol use.
Hierarchical Logistic Regression Analyses of Girls’ Lifetime Alcohol Use
Hierarchical Logistic Regression of Girls’ Recent (Past 30-Day) Alcohol Use
Hierarchical logistic regression model for girls’ lifetime drinking
Age and academic performance were included in Block 1 (Table 2). Although the model showed that the two background variables contributed to girls’ lifetime drinking (p < .0001), neither of the background variables made an individual contribution. Both variables were related to girls’ lifetime drinking when they were initially entered in the model. However, when psychological factors were included in Block 2, academic performance was no longer a predictor. The effect of age diminished in Block 3, when perceived peer alcohol use was entered in the model.
Block 2 examined the effects of psychological variables on girls’ drinking. Depressed girls were more likely to have drunk alcohol (p < .01) than less depressed girls. When girls were satisfied with their appearance and weight, they were less likely to have drunk (p < .05). Girls who had better self-efficacy were less likely to have drunk (p < .0001). The peer use variable was added to the regression equations at Block 3. The perception of peer alcohol use was positively associated with girls’ lifetime alcohol use (p < .0001).
Familial variables were entered in Block 4 and contributed to the model significantly (p < .0001). Of five familial factors, four demonstrated significant associations with girls’ lifetime alcohol use. Whereas maternal drinking was positively associated with girls’ lifetime use (p < .01), parental monitoring (p < .001), family rules against alcohol use (p < .05), and parental involvement (p < .05) were negatively associated with girls’ lifetime alcohol use.
The interactional analyses indicated that family rules against drinking moderated the association between peer drinking and girls’ drinking, and parental involvement and mother–daughter communication moderated the effects of body esteem on girls’ drinking (Figure 1). The relationship between peer drinking and girls’ drinking was weaker when the family had rules against drinking (p < .05). Among girls who had higher levels of body esteem, those whose parents were more involved and those who had more communication with their mothers were less likely to have drunk (both ps < .05).
Figure 1. Plots of the interactions between family rules and peer use (odds ratio [OR] = 0.87, confidence interval [CI] = 0.78–0.99; p < .05), family involvement and body esteem (OR = 0.94, CI = 0.88–0.99; p < .05), and mother–daughter communication and body esteem (OR = 0.96, CI = 0.93–0.99; p < .05) from the logistic regression analyses. Lines depict predicted girls’ lifetime alcohol use differences at 1 SD above and below the mean for corresponding family variables. For ease of interpretability, analyses for probing and graphing interactions did not include covariates.
Hierarchical logistic regression model for girls’ recent (past 30-day) drinking
The results of the regression model for recent drinking are displayed in Table 3. Again, neither background variable was significantly associated with girls’ alcohol use. Consistent with the findings of the lifetime alcohol use model, the significant contribution of academic performance diminished when psychological factors were included in Block 2, and the contribution of age diminished when perceived peer use of alcohol was entered in Block 3.
Psychological factors were included in Block 2. Whereas girls who were depressed were more likely to have recently drunk (p < .05), girls with better self-efficacy were less likely to have drunk (p < .0001). Body esteem did not make a significant contribution to girls’ recent alcohol use. The peer use variable was included in Block 3. Girls whose close friends drank alcohol were more likely to have drunk recently (p < .0001). Familial variables were added in Block 4 and contributed to the model significantly (p < .0001). However, of the familial variables, only maternal drinking made a significant individual contribution and was positively associated with girls’ recent alcohol use (p < .0001).
Interaction analyses indicated a relationship between mother–daughter communication and girls’ body esteem, self-efficacy, and peer drinking (Figure 2). Among girls who communicated with their mother more, increased body esteem (p < .05) and self-efficacy (p < .05) were associated with lower recent drinking. Girls who had more communication with mothers and had fewer drinking friends were less likely to have drunk recently (p < .001).
Figure 2. Plots of the interactions between mother–daughter communication and body esteem (odds ratio [OR] = 0.94, confidence interval [CI] = 0.89–0.99; p < .05), self-efficacy (OR = 0.91, CI = 0.83–0.98; p < .05), and peer alcohol use (OR = 1.06, CI = 1.02–1.10; p < .001) from logistic regression analyses. Lines depict predicted girls’ recent alcohol use differences at 1 SD above and below the mean for mother–daughter communication.
DiscussionStudy results confirmed our first set of hypotheses concerning the relationship between depression, body esteem, self-efficacy, peer alcohol use, and girl’s drinking. Higher levels of depression, lower self-efficacy, and greater levels of perceived peer alcohol use contributed to both girls’ lifetime and recent alcohol use. Girls’ dissatisfaction with their appearance and weight was positively associated with their lifetime drinking, albeit such a relationship was not replicated in the recent drinking model. Body esteem may have different functional roles during girls’ developmental processes. Warranting note is that body esteem may not be associated with alcohol consumption among adolescent girls until they enter late adolescence (i.e., 18 years; Rauste-von Wright, 1989).
Study data partially support our hypothesis that familial variables would exert distinct impacts on girls’ alcohol use when girls’ personal characteristics, psychological states, and perceived peer drinking were considered in the analysis. Beta weights indicate that parental monitoring, family rules against alcohol use, and parental involvement were associated with decreased girls’ lifetime alcohol use, but not recent use. Only maternal drinking was significantly related to both girls’ lifetime and recent alcohol consumption. Other work suggests that mothers may influence adolescent drinking by modeling drinking behavior (Dooley & Prause, 2007; Tyler, Stone, & Bersani, 2007). In our study, girls whose mother recently drank were 1.5 times more likely to have drunk alcohol in their lifetime, and were 2.8 times more likely to have drunk in the past month compared with girls whose mother who did not drink.
Our prediction that family domain variables would contribute to girls’ drinking above and beyond that accounted for by psychological and peer variables was supported. Controlling for individual and peer factors, inclusion of family domain variables improved the fit of lifetime and recent use models significantly, though the added effects were small.
The interaction analyses partially supported the premises of family interaction theory. Whereas maternal alcohol use and parental monitoring only showed direct effects on girls’ drinking and did not exert indirect effects, family rules against alcohol use, parental involvement, and mother–daughter communication appeared to buffer girls against factors that might increase their likelihood to drink. Despite bearing no direct effects on girls’ alcohol use in either regression model, mother–daughter communication moderated the effects of self-efficacy, body esteem, and peer alcohol use on girls’ drinking. These results highlighted the protective values of a warm information exchange style and open communication between mothers and daughters.
Study findings must be interpreted with caution. First, the cross-sectional design limits causal interpretations. Second, the generalizability of the results is compromised given the community sample of girls with private computer access, the use of a non-probability sampling strategy, and a moderate consent rate. Third, the study employed many brief measures. Fourth, the contribution of broader environmental factors (e.g., alcohol advertising, alcohol availability in the neighborhood) and interactions among psychosocial factors that may influence girls’ drinking cannot be disaggregated in our data. Fifth, the validity of self-reported data is questionable. Sixth, data were collected exclusively via the Internet.
Drawn from a large, ethnically diverse sample, study findings lend credence to previous results that alcohol use among adolescent girls is explained in part by individual, peer and family factors. In line with family interaction theory, the study suggests that familial factors not only directly impact girls’ drinking, but also that these factors may safeguard against peer and psychological risks. To be effective, alcohol misuse prevention programs for adolescent girls should begin early, involve parents, and address the interplay of risk and protective factors in multiple domains.
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Submitted: January 1, 2009 Revised: May 26, 2009 Accepted: May 27, 2009
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Source: Psychology of Addictive Behaviors. Vol. 23. (4), Dec, 2009 pp. 708-714)
Accession Number: 2009-24023-018
Digital Object Identifier: 10.1037/a0016681
Record: 77- Urgency traits and problematic substance use in adolescence: Direct effects and moderation of perceived peer use. Stautz, Kaidy; Cooper, Andrew; Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014 pp. 487-497. Publisher: American Psychological Association; [Journal Article] Abstract: Negative and positive urgency are facets of trait impulsivity that have been identified as possible risk factors for problematic substance use. Relationships between these traits and substance use measures have not yet been widely investigated in adolescents. In the current study, a sample of 270 adolescent students completed self-report measures of impulsivity-related traits, their alcohol and cannabis use, problematic use, and perceived peer use. Zero-inflated negative binomial regression models indicated that both urgency traits accounted for significant variance in problematic alcohol and cannabis use scores, even after accounting for nonurgency impulsivity traits and typical substance consumption. Furthermore, both urgency traits moderated the positive association between perceived peer alcohol use and individual problematic use. Results indicate that the urgency traits show a direct association with problematic substance use in adolescence, and that high urgency adolescents who believe their peers drink high levels of alcohol may be at increased risk of problematic alcohol use. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Urgency Traits and Problematic Substance Use in Adolescence: Direct Effects and Moderation of Perceived Peer Use
By: Kaidy Stautz
Department of Psychology, Goldsmiths, University of London, London, United Kingdom;
Andrew Cooper
Department of Psychology, Goldsmiths, University of London, London, United Kingdom
Acknowledgement:
Alcohol and cannabis use often begin during adolescence and prevalence rates increase almost linearly from the age of 12 to 18 (Fuller, 2012; Young et al., 2002). Although consumption of these substances is common, there are significant risks associated with their use. Individuals who begin using alcohol or cannabis at younger ages are at increased risk of developing a substance use disorder, and males who drink heavily in adolescence are at increased risk of premature death (Grant & Dawson, 1997; McCambridge, McAlaney, & Rowe, 2011; Winters & Lee, 2008; Zeigler et al., 2005). Those who initiate cannabis use in adolescence are more likely to become persistent users and appear to be at risk of developing problems with neuropsychological functioning as a result (Meier et al., 2012). There is also a cost to public services. In the United Kingdom, over 90% of under 18-year-olds seeking substance use treatment do so for problematic use of alcohol or cannabis or both (Lewis & Jones, 2012). The term problematic use refers to a pattern of substance use marked by negative consequences (Stice, Barrera, & Chassin, 1998). These may be short-term, for instance sustaining an injury while intoxicated, or long term, such as the development of abuse and dependence symptoms.
Research into risk factors for problematic substance use has isolated the dispositional trait of impulsivity as a reliable predictor of substance use and negative substance-related outcomes (Verdejo-García, Lawrence, & Clark, 2008). A contemporary view of the impulsivity construct acknowledges that there are multiple aspects of impulsive behavior, each of which can be represented by a distinct trait. The “UPPS” framework posits five impulsivity-related traits (Cyders & Smith, 2008; Whiteside & Lynam, 2001): lack of premeditation reflects the inability to consider the consequences of one’s actions; lack of perseverance reflects the inability to persist with a task; sensation seeking reflects an inclination toward novel and thrilling experiences; and negative and positive urgency reflect tendencies to act carelessly when in an exceptionally low or good mood, respectively.
Negative and positive urgency have been identified as particularly risky traits for problematic substance use. Theoretically, the traits neatly combine two aspects of behavior believed to be more prominent in those at heightened risk for substance use disorder: the inability to control one’s actions and the inability to regulate one’s emotions (Tarter et al., 2003). Empirically, negative urgency has been shown to relate to problematic alcohol and cannabis use and the number of other illicit substances tried (Fischer & Smith, 2008; Kaiser, Milich, Lynam, & Charnigo, 2012), and has been found to be elevated in substance dependent individuals (Verdejo-García, Bechara, Recknor, & Pérez-García, 2007). Positive urgency has been found to predict alcohol quantity consumed and problematic use over 1 year (Cyders, Flory, Rainer, & Smith, 2009), as well as increases in illegal drug use and risky sexual behavior (Zapolski, Cyders, & Smith, 2009). A recent meta-analysis of impulsivity-related traits and adolescent alcohol use found that positive and negative urgency showed the largest associations with measures of problematic drinking (Stautz & Cooper, 2013).
A limitation to this growing body of evidence is that much of the data have been collected from samples aged over 18. Alcohol and cannabis use are often initiated before this age (Kosterman, Hawkins, Guo, Catalano, & Abbott, 2000), and use of these substances can have detrimental effects on behavioral control and emotion regulation, the psychological underpinnings of the urgency traits (Cyders & Smith, 2008; Fox, Hong, & Sinha, 2008). Stautz and Cooper (2013) highlighted a dearth of research examining the urgency traits alongside alcohol use in samples younger than college age. One study that did use a younger sample found that negative and positive urgency were the UPPS traits most associated with onset of alcohol use in 9–13-year-olds (Gunn & Smith, 2010). It therefore seems necessary to examine these traits alongside a broader array of alcohol and cannabis use measures in an adolescent sample.
In addition to establishing direct relationships between the urgency traits and substance use outcomes, a number of studies have begun to investigate how these traits might moderate more proximal risk factors. Karyadi and King (2011) found that heightened positive urgency strengthened the relationship between depressive symptoms and negative consequences from alcohol use, and Burton, Pedersen, and McCarthy (2012) recently found that individuals high in the urgency traits showed a stronger relationship than low urgency individuals between positive implicit associations about alcohol use and actual alcohol use. To build upon these interesting findings, the present enquiry considered whether the urgency traits had a moderating effect on the association between perceived peer substance use and individual problematic use.
Peer substance use is perhaps the most salient environmental influence on individual use during adolescence (Swadi, 1999). Affiliation with substance using peers is consistently associated with elevated individual substance use in adolescents (Chassin, Curran, Hussong, & Colder, 1996; Cleveland, Feinberg, Bontempo, & Greenberg, 2008; Jenkins, 1996), and perceived peer alcohol use prospectively predicts individual alcohol use initiation and problematic drinking (Cardenal & Adell, 2000; Trucco, Colder, & Wieczorek, 2011). The social psychology literature provides a number of theories that help to explain this phenomenon. For example, the theory of planned behavior (Ajzen, 1988) posits that social norms are one of three informational sources that influence intentions to engage in certain behaviors. Similarly, a social learning approach (Bandura, 1977) would suggest that the association between individual and peer substance use is due to a process of modeling, whereby individuals learn and imitate the behavior of close acquaintances. These processes seem pertinent to adolescence. The adolescent brain is particularly responsive to social cues, showing heightened response to the rewarding aspects of social interaction as well as to the punishment of social rejection (Masten et al., 2009; Pfeifer et al., 2011). It has been speculated that postpubertal adolescents become more reactive to the affective information provided by social stimuli, but that the ability to regulate this reactivity matures more gradually (Nelson, Liebenluft, McClure, & Pine, 2005). Acting in contrast to social norms during this period is likely to be extremely challenging, perhaps especially so for impulsive individuals. Impulsivity measured by the Barratt Impulsiveness Scale (Patton, Stanford, & Barratt, 1995) shows a negative correlation with self-reported resistance to peer influence (Steinberg & Monahan, 2007).
To our knowledge, no studies have examined whether individual differences in the urgency traits are associated with perceived social norms regarding substance use. However, there is some evidence that individuals in a positive mood are prone to using normative information to guide their judgments (Armitage, Conner, & Norman, 1999; Forgas, 1995). A predisposition to act without thinking when in a heightened emotional state may exacerbate this process. Given the emotional salience of peer approval and rejection during adolescence, individuals high in urgency traits may be more inclined to use perceived peer norms to direct their behavior when in extreme mood states, and less likely to exercise constraint. Such a process could become particularly dangerous in high urgency individuals who perceive that their peers condone and encourage the use of alcohol and cannabis, as normative information could be used to guide substance use behavior at the expense of considering potential negative consequences.
The Current StudyThe aims of this study were: (a) to examine associations between impulsivity-related traits and aspects of alcohol and cannabis use in a sample of adolescents younger than college age, (b) to test whether the urgency traits account for unique variance in problematic alcohol and cannabis use, and (c) to test whether the urgency traits moderate relationships between perception of peer substance use and problematic use. We hypothesized that the urgency traits would be positively associated with problematic alcohol and cannabis use in this younger sample, and that they would account for unique variance in problematic use scores after accounting for other impulsivity-related traits and typical levels of use. For the moderation analyses, we predicted that high urgency adolescents would show stronger associations between perception of peer substance use and own problematic use than low urgency adolescents.
Method Participants
Participants were 270 sixth form students from two schools in east London, United Kingdom. The sample was 73% female and ranged in age from 16–18 with a mean of 16.79 (SD = .54). Seventy-two participants were aged 16, 179 were aged 17, 14 were aged 18, and five did not report their age. Data regarding ethnicity and socioeconomic status were not recorded. However, the schools are from ethnically diverse areas and this is represented in their overall student body. One school reported that around 80% of its students are from minority ethnic groups. The majority of participants (n = 228) were recruited from this school. The second reported that around half of its students are White British. Forty-two participants were recruited from this school.
Procedure
The study was approved by the Goldsmiths, University of London, Department of Psychology Ethics Committee. Twenty schools in the London area were contacted with information about the study and a request for participation. Representatives from the humanities departments of two schools agreed for their students to take part. Teachers from participating schools were provided with consent forms for students to give to their parents/guardians. A passive consent procedure was used whereby parents/guardians were informed about the study and given the option to exclude their children from participation. One individual was excluded.
Questionnaires were administered in groups of around 20 during class time under test conditions with the researcher and a teacher present. Participants gave written assent prior to completing the questionnaires. Once all participants in a group had completed the questionnaires, the group was debriefed and given the opportunity to ask questions about the study. Participants were given relevant Web site links to visit if the study had led them to become concerned about their substance use.
Measures
Impulsivity
The UPPS-P Impulsive Behavior Scale (Cyders et al., 2007; Whiteside & Lynam, 2001) is a 59-item measure assessing five facets of impulsivity. Items are assessed using a 4-point Likert-type response format, from 1 = I agree strongly to 4 = I disagree strongly, with the majority of items being reverse coded. The scales have been shown to display good convergent and discriminant validity (Smith et al., 2007). For the present study the mean response for each facet was calculated, giving a score between 1 and 4, where 4 indicates higher trait expression. The alpha reliabilities in the present sample were: lack of premeditation = .84, lack of perseverance = .77, sensation seeking = .86, negative urgency = .83, positive urgency = .90.
Alcohol use
Typical alcohol consumption was assessed with two items, adapted from the Alcohol Use Disorders Identification Test (Babor, Higgins-Biddle, Saunder, & Monteiro, 2001): How often do you have a drink containing alcohol? and How many drinks containing alcohol do you have on a typical day when you are drinking? Participants were asked to consider their responses with regard to the past year. Five response options were provided for each question, ranging from 0 = Never to 4 = 4 or more times per week, and 0 = 1 or 2 drinks to 4 = 10 or more drinks, respectively. In line with previous research (Sobell & Sobell, 2003), the product of these two item scores was computed to give a continuous score between 0 and 16.
Perceived peer alcohol use was assessed using five questions. The content of this scale reflected both descriptive norms, assessed with the items: How many of your friends do you think have had any alcohol to drink in the last 30 days?; How many of your friends would you estimate get drunk at least once a week?; When your close friends drink, how much, on average, does each person drink?; and injunctive norms, assessed with the items: How do your friends feel about drinking alcohol?; and How do your friends feel about getting drunk? Five response options were provided for each question, ranging from 0 = None to 4 = 7 or more for the first two questions, 0 = 1 or 2 drinks to 4 = 10 or more drinks for the third question, and 0 = They all disapprove to 4 = They all approve for the fourth and fifth questions. To clarify the factor structure of these five items, an exploratory factor analysis with maximum likelihood extraction and direct oblimin rotation was conducted. One factor was extracted, which accounted for 61.05% of the total variance. It was therefore feasible to combine scores of the five items, summing them for one continuous score between 0 and 20. The alpha reliability for this scale was .84.
Cannabis use
Typical cannabis consumption was assessed with the item How often do you smoke cannabis? Participants were asked to consider their response with regard to the past year. Five response options were provided, ranging from 0 = Never to 4 = 4 or more times per week.
Perceived peer cannabis use was assessed using four questions. As with perceived alcohol use, item content reflected descriptive norms: How many of your friends do you think have used cannabis in the last 30 days?; How many of your friends would you estimate use cannabis at least once a week?; When your close friends smoke cannabis, how much, on average, does each person smoke?, with “cannabis cigarettes” as the quantity unit; and injunctive norms: How do your friends feel about smoking cannabis? Five response options were provided for each question, following a similar format to the alcohol use questions. An exploratory factor analysis of these four items extracted one factor, which accounted for 77.81% of the variance. Scores for the four items were summed for a continuous score between 0 and 16. The alpha reliability for this measure was .90.
Problematic alcohol use
Problematic drinking was assessed using the 18-item Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989; White & Labouvie, 2000). Items on the RAPI ask about negative consequences experienced from drinking alcohol during the past year. Sample items include Neglected your responsibilities, Felt that you had a problem with alcohol, and Felt physically or psychologically dependent on alcohol. It is scored using a 4-point Likert-type format ranging from 0 = None to 3 = More than 5 times. Item scores were summed, giving a continuous score ranging from 0 to 54. The RAPI has been found to have strong predictive validity, with scores predicting diagnosis for alcohol dependence 7 years after administration (Dick, Aliev, Viken, Kaprio, & Rose, 2011). The alpha reliability for the RAPI in this sample was .84.
Problematic cannabis use
Problematic cannabis use was assessed using the short form of the Cannabis Problems Questionnaire for Adolescents (CPQ-A-S; Proudfoot, Vogl, Swift, Martin, & Copeland, 2010), a 12-item scale with a yes/no response format. One point is scored for each item marked yes. The CPQ-A-S items ask whether respondents have experienced situations that can result from excessive cannabis use, such as passing out or feeling paranoid after a smoking session, during the past 3 months. Item scores were summed for a continuous score ranging from 0 to 12. Proudfoot, Vogl, Swift, Martin, and Copeland (2010) found this scale to have good convergent validity. The alpha reliability for the CPQ-A-S in this sample was .71.
Data Analysis
Data were analyzed using IBM SPSS version 19 and R version 3.0.0 with the pscl (Jackman, 2008; Zeileis, Kleiber, & Jackman, 2008) and MASS (Venables & Ripley, 2002) packages. An alpha level of p = .05 was used for significance testing. Missing value analysis established that no impulsivity-related trait scores were missing more than 5%. To maximize the amount of data that could be used, missing values for trait scores were imputed using expectation maximization. Three univariate outliers were identified. These appeared to be extreme scores and were Winsorized. One multivariate outlier showed extreme scores on a number of variables and was removed from analysis. Age was strongly restricted in range and was not included in regression analyses.
ResultsThe percentage of participants who reported ever using alcohol in this sample was 44.4%, less than is observed in the general British population for similarly aged children (Fuller, 2012). The percentage of participants who reported ever using cannabis was 20.7%. This is comparable to United Kingdom population data, which indicate that 17.1% of 16–24 year olds in the United Kingdom reported past year cannabis use (Smith & Flatley, 2011). Independent t tests showed that participants from School 2 had significantly higher mean perception of peer alcohol use scores (M = 9.17, SD = 4.10) than those from School 1 (M = 7.42, SD = 4.52), t(236) = 2.30, p = .02. No other significant differences between schools were found.
Descriptive statistics and bivariate correlations are presented in Table 1. The means and standard deviations presented were calculated using data from all participants, including those with zero scores on alcohol and cannabis measures. Among alcohol users only, the mean score for typical consumption was 2.59 (SD = 2.45) and for problematic use was 3.93 (SD = 5.26), indicating that the alcohol users in this sample drank at modest levels with a low prevalence of negative consequences over the past year. Among cannabis users only, the mean score for cannabis use frequency was 1.15 (SD = 1.15), and for problematic use was 2.13 (SD = 2.30), indicating that cannabis users in this sample used cannabis monthly or less on average and had experienced around two negative consequences from use in the past 3 months. Correlations between trait scores and substance use measures were analyzed for the whole sample and for users only. This analysis revealed a substantially increased correlation between positive urgency and problematic alcohol use in alcohol users (r = .39, p < .001), and between positive urgency and problematic cannabis use in cannabis users (r = .35, p = .01).
Bivariate Correlations and Descriptive Statistics
Negative and positive urgency scores showed a considerable intercorrelation, as is consistently found (Curcio & George, 2011; Simons, Dvorak, Batien, & Wray, 2010; Zapolski et al., 2009). We considered combining the two scores into a single urgency facet, yet noted potentially important differences in their correlations to problematic use scores, particularly problematic alcohol use. The difference between the negative urgency-problematic alcohol use and positive urgency-problematic alcohol use correlations was tested using Steiger’s z-Test (Steiger, 1980). The difference approached significance for the entire sample (z = 1.91, p = .06), and was significant for alcohol users only (z = 2.89, p = .003).
Due to a large proportion of zero scores, substantial skewness and kurtosis were observed for problematic alcohol use scores (S = 3.15, K = 10.62) and problematic cannabis use scores (S = 3.41, K = 11.69). The distributions for both variables approximated a negative binomial distribution with the exception of a large number of zero values. Zero-inflated negative binomial (ZINB) regression was therefore selected as the method of analysis for these data (Atkins & Gallop, 2007; Lewis et al., 2010; Simons, Neal, & Gaher, 2006). A ZINB regression model is a two-component mixture model that is capable of handling excess zero values. A negative binomial regression model is fit to the data, and excess zeros are simultaneously modeled with a logistic regression. The logistic portion of the model predicts zero scores that exceed the amount expected in a negative binomial distribution. The count portion of the model predicts values on the distribution and includes positive integers and zero.
We note that an assumption of the ZINB model is that the underlying distribution is made up of count data. Scores on the RAPI measure are not true count data as response choices are ordinal, that is, None, 1–2 times, 3–5 times, and More than 5 times. However, as Light et al. (2011) observe, RAPI scores do share certain properties with count distributions, namely that the data is non-negative with skewed integer values. Light et al. (2011) modeled RAPI data with seven types of regression model, including Ordinary Least Squares with and without transformation, and Poisson and negative binomial models with and without zero-inflation. ZINB models were found to fit their data best. We arrived at the same conclusion with the present data, as is reported below. Nevertheless, we suggest caution when interpreting the reported incident rate ratios for RAPI data. These ratios reflect how individual RAPI scores change as a function of predictor variables. Due to the nature of the RAPI, this is not necessarily identical to an increased count of problematic use symptoms.
Problematic Alcohol Use
Two ZINB regression analyses were conducted using problematic alcohol use as the criterion variable, with separate models for negative and positive urgency due to their high intercorrelation (see Table 2). Gender, lack of premeditation, lack of perseverance, sensation seeking, negative or positive urgency, typical alcohol use, and perceived peer alcohol use were entered as predictors at Step 1. These variables were mean-centered for this analysis. The product term of negative or positive urgency and perceived peer use was entered at Step 2. For the logistic portion of each model, typical alcohol use was not included as a predictor as the majority of participants reporting no alcohol use also reported no problematic use. As previously reported by Lewis et al. (2010), including a typical use score in a logistic regression of problematic use can lead to problems with separation, whereby one variable perfectly predicts the outcome, leading to unstable coefficients. For both ZINB models, a likelihood ratio test of overdispersion was significant, χ2(15) = 988.15, p < .001, suggesting that negative binomial models were preferable over Poisson models.
Zero-Inflated Negative Binomial Regression Analysis—Problematic Alcohol Use
The likelihood ratio for the negative urgency ZINB model was χ2(15) = 97.33, p < .001, indicating that the model was significant. The Vuong non-nested test supported the use of a zero-inflated model over a standard negative binomial model, z = 5.81, p < .001. For the logistic portion of this model, excess zero scores were significantly predicted by low perceived peer alcohol use and low lack of premeditation. For the count portion of the model, increased problematic alcohol use scores were significantly predicted by alcohol consumption and by negative urgency. The interaction term of negative urgency and perceived peer use was significant. Simple slopes analysis indicated that at −1 standard deviation of negative urgency scores the slope of the relationship between perceived peer alcohol use and problematic alcohol use was B = −.11, SE B = .05, z = −2.21, p = .03 (incident rate ratio = 0.90, 95% CI [0.82, 0.99]), and at + 1 standard deviation of negative urgency scores the slope was B = .10, SE B = .05, z = 1.93, p = .05 (incident rate ratio = 1.11, 95% CI [1.00, 1.23]). These relationships are displayed in Figure 1.
Figure 1. Moderation effect of negative urgency on the relationship between perceived peer alcohol use and problematic alcohol use.
The likelihood ratio for the positive urgency ZINB model was χ2(15) = 108.93, p < .001, indicating that the model was significant. The Vuong test supported the use of a zero-inflated model over a standard negative binomial model, z = 6.12, p < .001. For the logistic portion of this model, excess zero scores were significantly predicted by low perceived peer alcohol use and low lack of premeditation. For the count portion of the model, increased problematic alcohol use scores were significantly predicted by alcohol consumption and by positive urgency. The interaction term of positive urgency and perceived peer use was significant. Simple slopes analysis indicated that at −1 standard deviation of positive urgency scores the slope of the relationship between perceived peer alcohol use and problematic alcohol use was B = −.10, SE B = .05, z = −2.08, p = .04 (incident rate ratio = 0.90, 95% CI [0.82, 0.99]), and at +1 standard deviation of positive urgency scores the slope was B = .15, SE B = .06, z = 2.55, p = .01 (incident rate ratio = 1.16, 95% CI [1.04, 1.30]). These relationships are displayed in Figure 2.
Figure 2. Moderation effect of positive urgency on the relationship between perceived peer alcohol use and problematic alcohol use.
The beta values of positive and negative urgency in the count portions of these models were compared with the z test advocated by Paternoster, Brame, Mazerorolle, and Piquero (1998). This revealed no significant difference, z = 1.06, p = .28. The interaction terms in the count portions of the models were also compared, with no significant difference found, z = .33, p = .74.
Lack of premeditation was found to be a significant negative predictor of problematic alcohol use in the count portion of these two models, in contrast to the positive bivariate correlation observed between these variables. This appeared to be due to a suppression effect (MacKinnon, Krull, & Lockwood, 2000). Lack of premeditation was highly correlated with lack of perseverance and the urgency traits. In a model with these traits removed, lack of premeditation showed a nonsignificant positive association with problematic use scores (B = 0.07, SE B = 0.23, z = 0.29, p = .77).
Problematic Cannabis Use
Two further ZINB regression analyses were conducted using problematic cannabis use as the criterion variable, with separate models for negative and positive urgency (see Table 3). Gender, lack of premeditation, lack of perseverance, sensation seeking, negative or positive urgency, cannabis use frequency, and perceived peer cannabis use were entered as predictors at Step 1. The product term of negative or positive urgency and perceived peer use was entered at Step 2. For the logistic portion of each model, cannabis use frequency was not included as a predictor as the majority of participants reporting no cannabis use also reported no problematic use. For both models, a likelihood ratio test of overdispersion was significant, χ2(15) = 273.97, p < .001, suggesting that negative binomial models were preferable over Poisson models.
Zero-Inflated Negative Binomial Regression Analysis—Problematic Cannabis Use
The likelihood ratio for the negative urgency ZINB model was χ2(15) = 140.46, p < .001, indicating that the model was significant. The Vuong test supported the use of a zero-inflated model over a standard negative binomial model, z = 6.24, p < .001. For the logistic portion of this model, excess zero scores were significantly predicted by low perceived peer cannabis use only. For the count portion of the model, increased problematic cannabis use scores were significantly predicted by negative urgency and by lack of perseverance. The interaction term of negative urgency and perceived peer use was not significant.
The likelihood ratio for the positive urgency ZINB model was χ2(15) = 125.65, p < .001, indicating that the model was significant. The Vuong test supported the use of a zero-inflated model over a standard negative binomial model, z = 5.59, p < .001. For the logistic portion of this model, excess zero scores were significantly predicted by low perceived peer cannabis use only. For the count portion of the model, increased problematic cannabis use scores were significantly predicted by positive urgency. The interaction term of positive urgency and perceived peer use was not significant.
The beta values of positive and negative urgency in the count portions of these two models were compared, revealing no significant difference, z = 1.62, p = .11. Lack of premeditation was found to be a significant negative predictor of problematic cannabis use in the count portion of these two models. As with problematic alcohol use, a reduced model excluding lack of perseverance and the urgency traits was fitted to test for suppression effects. In this model lack of premeditation showed a nonsignificant negative association with problematic cannabis use (B = −0.43, SE B = 0.25, z = −1.74, p = .08).
DiscussionThis study aimed to determine how the five impulsivity-related personality traits specified by the UPPS framework were associated with aspects of alcohol and cannabis use in an adolescent sample. A particular focus was given to negative and positive urgency, traits that have previously been shown to relate to problematic alcohol and substance use in older samples.
Of the five UPPS traits, positive urgency showed the largest correlation with measures of problematic alcohol use. This association was substantially larger when the analysis was limited to participants who were alcohol users. Both urgency traits were found to explain a significant amount of variance in problematic alcohol use scores, even after accounting for other nonurgency impulsivity-related traits and typical consumption. These findings indicate direct associations between the urgency traits and problematic alcohol use that are not explainable by increased levels of consumption. This is in line with the observation that urgency traits show stronger relationships with problematic use measures than with typical consumption in older samples (Curcio & George, 2011; Kaiser et al., 2012; Simons et al., 2010), and suggest that the urgency traits may be psychological characteristics that help to distinguish problematic alcohol users from typical, nonproblematic users (Colder & Chassin, 1999). However, the finding is somewhat inconsistent with data from college age participants, which show an indirect effect of positive urgency on problematic use through heightened alcohol consumption (Wray, Simons, Dvorak, & Gaher, 2012). It may be that high urgency adolescents are particularly susceptible to negative consequences at relatively low levels of typical consumption, perhaps as a consequence of failing to consider the possible dangerous outcomes that can result from alcohol use (Van Hoof, Van den Boom, & De Jong, 2011).
Both urgency traits moderated the relationship between perceived peer alcohol use and individual problematic use, making it stronger. This is a novel finding. Prior research has established a link between perceived drinking norms and problematic use (Beck & Treiman, 1996; Cardenal & Adell, 2000; Martino, Ellickson, & McCaffrey, 2009), yet there has been limited consideration of dispositional factors that might exacerbate this link. Our data suggest that adolescents high in urgency may be particularly likely to show a link between the perceived drinking norms of their peers and their own problematic use. Interestingly, the urgency traits were not significantly associated with perception of peer alcohol use, suggesting that the observed interaction effect is unlikely to be the result of high urgency individuals socializing with more alcohol-using peers or overestimating peer drinking norms.
Urgency appears to reflect a tendency to rely on affective input to guide behavior, at least when in a heightened mood state. High urgency individuals may be more inclined to use the “affect heuristic” (Slovic, Finucane, Peters, & MacGregor, 2002), that is, to make decisions based on the affective valence of representations associated with those decisions. Phillips, Hine, and Marks (2009) found that adolescents with high negative urgency scores showed a stronger relationship than those with low scores between their affective associations about drinking alcohol and self-reported binge drinking. As social stimuli appear to activate a strong emotional response in adolescence (Nelson et al., 2005), normative information about alcohol use could act as one such affective association. Those who perceive high peer use may use this information as a positively valenced affective influence on their decision to consume alcohol. This, combined with a reduced tendency to control behavior when in extreme mood states, may spur dangerous alcohol use in high urgency adolescents.
Both urgency traits showed significant positive correlations with cannabis use frequency, and both explained significant variance in problematic cannabis use scores when controlling for other impulsivity-related traits and typical cannabis use. This is the first time that these relationships have been identified in an adolescent sample. These findings contribute to previous evidence of a positive relationship between urgency and cannabis use (Kaiser et al., 2012; Lynam & Miller, 2004), and support the possibility that difficulties with affect regulation are related to experiencing negative consequences from using cannabis (Simons & Carey, 2002). We did not find evidence of interaction effects between the urgency traits and perceived peer cannabis use on problematic use in this sample.
Limitations
This study was cross-sectional in nature, therefore our data do not confirm that the urgency traits are risk factors for problematic substance use in adolescence. Variables can only be considered risk factors once they are found to precede and reliably predict the outcome under investigation (Kraemer et al., 1997). Although the urgency traits have been shown to longitudinally predict problematic alcohol use and illegal substance use in older samples (Cyders et al., 2009; Zapolski et al., 2009), such prospective relationships are still to be confirmed in adolescents.
Generalization of our findings is somewhat limited by the high number of females in the present sample. This overrepresentation is reflective of humanities subjects in United Kingdom sixth forms (Department for Education & Skills, 2007), but may indicate a sampling bias in that representatives from humanities departments may have been more likely than those from other departments to respond to requests for participation in a psychological study. Additionally, the lack of data for participant ethnicity precluded comparisons being made between ethnic groups. Differences in substance use behavior between ethnic groups have been found at this age, with White adolescents reporting higher levels of heavy alcohol use and cannabis use than their African American peers in the United States (Johnston, O’Malley, Bachman, & Schulenberg, 2012), and higher past week alcohol use than their Black and Asian peers in the United Kingdom (Fuller, 2012). Data on socioeconomic status were also not collected. Future research could investigate how the relationships observed here are affected by ethnicity and sociocultural factors.
A further limitation was the sole use of self-report when assessing peer substance use. Numerous studies have reported the tendency of young people, mainly college students, to have an exaggerated perception of their peers’ substance use (see Perkins, 2002). Obtaining reports from participants’ friends on their actual alcohol and cannabis use in order to verify whether individual reports are accurate would be a useful addition in future work.
Finally, the analysis model used was not entirely appropriate for RAPI data, despite being the best fitting option (Light et al., 2011). We recommend that researchers using substance use measures with samples in which a high number of zero values are expected show careful consideration in selecting suitable methods of assessment and analysis. One possible remedy would be to modify response options for the RAPI and similar scales so that scores reflect a true count distribution.
Future Directions
Further investigation into how heightened urgency increases risk for problematic substance use is warranted. The present findings suggest that adolescents high in urgency traits may be particularly susceptible to certain social factors influencing dangerous drinking behavior, perhaps because of these factors’ affective salience. Authors have emphasized the role of affect in decision making under risk generally (Loewenstein, Weber, Hsee, & Welch, 2001), and in progression toward substance dependence specifically (Murphy, Taylor, & Elliott, 2012). Investigating potential errors and biases in the decision making of high urgency adolescents and how these might come about in certain emotional states or social contexts is therefore encouraged.
Additionally, examining interactions between urgency and perceived peer behavior in relation to other substances such as nicotine may yield interesting results. Urgency traits have been found to relate to nicotine dependence and craving in college students (Billieux, Van der Linden, & Ceschi, 2007; Spillane, Smith, & Kahler, 2010), but it has yet to be shown whether these traits have any moderating influence on peer-related risk factors for problematic smoking.
ConclusionOur findings indicate that negative and positive urgency are the elements of trait impulsivity most related to problematic alcohol and cannabis use in adolescence. These appear to be direct effects, not fully explained by increased use frequency. Additionally, this study extends the literature by highlighting moderating effects of the urgency traits on the relationship between perceived peer alcohol use and individual problematic use. We encourage other researchers to further investigate how the urgency traits influence problematic substance use and other risk behaviors.
Footnotes 1 We thank an anonymous reviewer for raising these points.
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Submitted: January 29, 2013 Revised: July 31, 2013 Accepted: August 5, 2013
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Record: 78- Which facets of mindfulness predict the presence of substance use disorders in an outpatient psychiatric sample? Levin, Michael E.; Dalrymple, Kristy; Zimmerman, Mark; Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014 pp. 498-506. Publisher: American Psychological Association; [Journal Article] Abstract: There have been inconsistent findings regarding the relationship of mindfulness to substance use disorders, which may be attributable in part to measurement issues and the use of nonclinical samples. The current study examined the relationship between specific facets of mindfulness and substance use disorders (SUD) in a clinical sample. The sample consisted of 867 patients seeking outpatient treatment and who completed diagnostic interviews and self-report assessments. Results indicated that deficits in acting with awareness, being nonjudgmental, and nonreactivity were related to the presence of a current SUD relative to those with no history of SUD, although only acting with awareness and being nonjudgmental were related when all of the facets were included in a logistic regression. Patients with a past history of SUD had greater deficits in acting with awareness relative to those with no history of SUD. Results were similar when examining alcohol use and drug use disorders separately. Current nicotine users had greater deficits in being nonjudgmental, but not on other mindfulness facets. The observing facet was not related to current or past history of SUD. The results of the study and future directions are discussed in relation to research on mindfulness-based treatments for addiction. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Which Facets of Mindfulness Predict the Presence of Substance Use Disorders in an Outpatient Psychiatric Sample?
By: Michael E. Levin
Department of Psychology, Utah State University;
Kristy Dalrymple
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University and Department of Psychiatry, Rode Island Hospital, Providence, Rhode Island
Mark Zimmerman
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University and Department of Psychiatry, Rode Island Hospital, Providence, Rhode Island
Acknowledgement:
Mindfulness-based methods have been gaining increasing attention as an approach for treating substance abuse and other addictive behaviors (Bowen, Chawla & Marlatt, 2010; Hayes & Levin, 2012; Zgierska et al., 2009). These interventions focus on various techniques such as contemplative meditation to improve one’s awareness of the present moment in a nonjudgmental, nonreactive, and accepting way. There is a growing research base suggesting that mindfulness-based interventions, as well as interventions that incorporate mindfulness technologies within a cognitive–behavioral approach, may be efficacious for treating substance abuse and preventing relapse (Bowen, Witkiewitz & Chawla., 2012; Chiesa & Serretti, 2013; Kelly et al., 2012; Zgierska et al., 2009).
Although there have been a number of clinical trials evaluating mindfulness-based interventions with addictions (see Chiesa & Serretti, 2013 for a recent review), there has been limited research regarding whether deficits in mindfulness skills are related to substance use disorders (SUDs) in nonintervention research. Such research could inform the development of a theoretical model for understanding how mindfulness leads to improvements in addiction outcomes, guiding further treatment innovations. More generally, nonintervention studies have found that deficits in mindfulness are predictive of depression, anxiety, and a variety of other measures of psychological health (Keng et al., 2011). Research with cigarette smokers has similarly found that deficits in mindfulness are related to severity of nicotine dependence and withdrawal symptoms (Vidrine et al., 2009). The research to date examining the relationship between mindfulness and alcohol/illicit drug abuse has been more inconsistent, however, with some studies finding deficits in mindfulness relating to higher rates of substance abuse (Fernandez, Wood, Stein & Rossi, 2010; Murphy & MacKillop, 2012) and other studies finding the opposite such that deficits relate to lower rates of substance abuse (Leigh & Neighbors, 2009; Leigh, Bowen & Marlatt, 2005).
One explanation for the inconsistent findings with substance abuse is variations across studies in how mindfulness is measured. Early self-report measures tended to consist of a single factor assessing overall levels of mindfulness (e.g., Mindful Attention Awareness Scale, Brown & Ryan, 2003). More recent research has found that the factor structure of mindfulness self-report items indicates a multifaceted construct (Baer et al., 2006). There are a variety of conceptualizations of mindfulness, but definitions tend to highlight facets including awareness of the present and taking a nonjudgmental, accepting, and nonreactive stance to whatever is observed (Bishop et al., 2004). The current study focused on the mindfulness facets derived from the Five Facet Mindfulness Questionnaire (FFMQ; Baer et al., 2006), which consists of observing, describing, acting with awareness, being nonjudgmental, and nonreactivity, and thus provides a means to examine the relation of SUDs to a range of theoretically meaningful facets. When examining specific facets of mindfulness, it appears that higher scores on a measure of mind/body awareness tended to relate with greater substance use (Leigh et al., 2005; Leigh & Neighbors, 2009), whereas measures assessing being nonreactive and nonjudgmental of one’s experiences, acting with awareness, and describing one’s experiences tend to be related to lower substance use (Fernandez et al., 2010; Leigh & Neighbors, 2009; Murphy & MacKillop, 2012).
Theoretically, deficits in each facet of mindfulness may play a role in substance use disorders including alcohol, illicit drug, and nicotine use disorders. Present moment awareness, including observing, describing, and acting with awareness, is important for identifying triggers and high risk situations, which can inform effective coping strategies in the moment and reduce the potential for impulsively or automatically using substances in reaction to triggers (Bowen et al., 2012). In addition, recent research suggests that a greater capacity to describe and differentiate one’s emotions throughout the day can serve to reduce substance abuse, even when experiencing negative affect (Kashdan & Rottenberg, 2010). Negative affect is a key predictor of relapse (e.g., Witkiewitz & Villarroel, 2009), and research indicates that individuals with substance use disorders have difficulty tolerating distress (Leyro, Zvolensky & Bernstein, 2010) and effectively regulating emotions (Aldao, Nolen-Hoeksema & Schweitzer, 2010). The capacity to be nonjudgmental and nonreactive to one’s experiences can serve to reduce emotional reactivity and ineffective coping strategies while providing a foundation to effectively practice tolerating distress and urges to use substances (Bowen et al., 2012). For example, one study found that individuals who were more mindfully accepting were less likely to abuse alcohol despite having implicit biases to do so (Ostafin & Marlatt, 2008). Given the inconsistent findings within research thus far, it is unclear which of these mindfulness facets play a role in substance abuse. Further research is needed to examine whether describing, acting with awareness, being nonjudgmental, and nonreactivity are all functionally important to SUDs or whether particular mindfulness facets are more or less relevant.
The current study from the Rhode Island Methods to Improve Diagnostic Assessment and Services (MIDAS) Project sought to examine which facets of mindfulness, if any, are related to the presence of SUDs among a clinical sample of psychiatric outpatients. Research to date primarily has been conducted with nonclinical samples, which may further explain the inconsistent findings across studies, and it is less clear how mindfulness facets relate to SUDs in clinical samples. Given past research, it was hypothesized that deficits in the describing, acting with awareness, nonreactivity, and nonjudgmental facets of mindfulness would all be related to the presence of SUDs. The observing facet was hypothesized to be unrelated to SUDs or relate in the opposite direction (i.e., greater observing scores related to higher probability of SUD), given past research suggesting this subscale is not predictive of psychopathology (e.g., Bohlmeijer et al., 2011) or predicts greater problems (e.g., Baer et al., 2006) and that heightened mind-body awareness relates to greater substance abuse (Leigh et al., 2005; Leigh & Neighbors, 2009). These results could further inform theoretical models regarding the role of specific mindfulness facets in addictions. In addition, these findings could help guide treatment development by highlighting the qualities of mindfulness that may be particularly important to focus on in SUD interventions.
Method Participants
The sample consisted of 867 patients seeking outpatient treatment at the Rhode Island Hospital Department of Psychiatry. The sample was 56.2% female with a median age of 39 (M = 39.29, SD = 14.24). The sample was 91.2% White/Caucasian, 3.3% Black/African American, 2.9% Hispanic, 1.6% Asian, and 1.0% other race. In addition, 3.6% of the sample identified as Latino ethnicity. In terms of education, 8.5% of the sample did not graduate high school, 52.3% of the sample graduated high school but not college, 24.5% graduated with a 4-year college degree, and 14.6% received a graduate degree. The percentage of patients with a current SUD diagnosis (excluding nicotine dependence) was 4% (n = 32) alcohol abuse, 7% (n = 60) alcohol dependence, 3% (n = 25) drug abuse, and 7% (n = 57) drug dependence. The percentage with a past SUD diagnosis was 16% (n = 134) alcohol abuse, 12% (n = 102) alcohol dependence, 12% (n = 100) drug abuse, and 10% (n = 83) drug dependence. The most common drug use disorders were cannabis abuse (2% with current diagnosis [n = 21]; 8% past diagnosis [n = 69]) and cannabis dependence (4% with current diagnosis [n = 31]; 4% past diagnosis [n = 32]). In addition, 3% were diagnosed with a current noncannabis drug use disorder and 12% diagnosed with a past noncannabis drug use disorder spanning across a range of illicit drugs. In terms of cigarette smoking prevalence, 23% were current smokers, 28% were past smokers, and 48% had no history of regular smoking (nicotine use disorders were not included as SUDs for reported analyses, but were analyzed separately). The prevalence of other psychological disorders by SUD status is provided in Table 1.
Prevalence of Current and Past Diagnoses by Substance Use Disorder Status
Procedure
The MIDAS project (Zimmerman, 2003) is integrated within an outpatient psychiatry practice providing fee-for-service psychiatric treatment, to patients with medical insurance (including Medicare but not Medicaid). The majority of referrals come from primary care physicians (31.6%), therapists in the community (15.8%), and family members or friends (17.5%); this latter group refers more than therapists. Patients seeking treatment at the practice are asked to participate in a comprehensive diagnostic interview prior to meeting with their treating clinician. For those who agree, informed consent is obtained before administering the diagnostic interview. The study was approved by the Rhode Island Hospital Institutional Review Board.
Diagnostic interviews were conducted by doctoral level clinical psychologists and bachelor’s level research assistants. Training for research assistants included observing a psychologist conduct at least 20 interviews, followed by being supervised while conducting 20 interviews. Training for clinical psychologists included observing 5 evaluations, followed by being supervised while conducting 15–20 evaluations. All interviewers then had to demonstrate almost perfect reliability on 5 interviews with senior diagnosticians to complete their training (i.e., agree on the principal diagnosis and demonstrate approximately 80% agreement on diagnoses overall for a given patient). Every interview is reviewed on an item-by-item basis with the senior diagnostician who observed the interview during the course of training. To prevent rater drift, ongoing weekly case conferences are conducted with all members of the team, and item ratings are reviewed by the principal investigator (M.Z.) for every case.
Measures
Clinical interviewer assessments
Diagnostic interviewers conducted the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM–IV; SCID; First, Spitzer, Gibbon & Williams, 1995) with each participant to assess DSM–IV Axis I disorders including substance use disorders. Interrater reliability on the SCID was examined for a subsample of patients (n = 65) using the joint interview method, with results indicating adequate reliability for alcohol use (k = .64) and drug use disorders (k = .73; Zimmerman, McGlinchey, Chelminski & Young, 2008). For the current study we combined the alcohol abuse, alcohol dependence, drug abuse, and drug dependence diagnostic categories into a single diagnostic group of substance use disorders (SUD).
Cigarette smoking history was also assessed during the clinical interview with the question “Have you ever smoked cigarettes daily for at least one month?” Follow-up questions determined whether patients were currently smoking regularly or only had a past history of cigarette smoking.
Additional interviewer items examined in the current study included the psychic and somatic anxiety items from the Schedule for Affective Disorders and Schizophrenia (SADS; Spitzer & Endicott, 1977), which were used to assess severity of anxiety symptoms. The psychic anxiety item asks “how anxious, frightened, scared, or apprehensive have you felt during the past week?” and the somatic anxiety item asks “During the past week have you been bothered by physical symptoms like palpitations, shortness of breath, sweating, headaches, stomach cramps or muscle tension?” For both of these items, patient responses are rated on a 6-point scale ranging from 0, Not at all to 5, Extreme. The interrater reliability for the psychic and somatic anxiety items were ICC = .88 (p < .001) and ICC = .87 (p < .001), respectively. In addition, the Clinical Global Impression-Severity of depression (CGI-S; Guy, 1976) was used to rate overall depressive symptom severity, with severity rated on a 6-point scale ranging from 0, None to 5, Extreme. Interrater reliability for the CGI was also high (ICC = 0.79, p < .001).
Five Facet Mindfulness Questionnaire (FFMQ; Baer et al., 2006)
The FFMQ is a 39-item questionnaire designed to assess five facets of mindfulness: observing, describing, acting with awareness, being nonjudgmental, and nonreactivity. The observing subscale assesses one’s attention to present moment experiences (i.e., “I notice the smells and aromas of things”). The describing subscale assesses the ability to label one’s thoughts, feelings, and sensations (i.e., “I’m good at finding the words to describe my feelings”). The acting with awareness subscale assesses the tendency to not attend to present moment experiences while engaging in activities (i.e., “I find myself doing things without paying attention”). The nonjudgmental subscale assesses the tendency to judge one’s thoughts and feelings as bad and to criticize oneself for having negative thoughts and feelings (i.e., “I think some of my emotions are bad or inappropriate and I shouldn’t feel them”). The nonreactivity subscale assesses the capacity to notice thoughts and feelings without acting on or otherwise getting entangled with them (i.e., “I perceive my feelings and emotions without having to react to them”).
Each FFMQ item is rated on a 5-point scale ranging from 1 (never or very rarely true) to 5 (very often or always true). Negatively worded items were reverse scored so that for each subscale higher scores indicated greater mindfulness. The FFMQ has been found to be a reliable and valid measure of mindfulness in past research with nonclinical (Baer et al., 2006) and clinical samples (Bohlmeijer et al., 2011). In the current sample, each of the subscales demonstrated adequate internal consistency (Observing Cronbach’s alpha = .81; Describing Cronbach’s alpha = .89; Acting with awareness Cronbach’s alpha = .90; Nonjudgmental Cronbach’s alpha = .91; and Nonreactivity Cronbach’s alpha = .81).
Data Analysis
The FFMQ subscales were normally distributed as indicated by examining histograms, skewness, and kurtosis for each variable. For each subscale, mean scores were calculated provided a participant answered 85% or more of the items for the given subscale, which has been used in past research (Levin, Lillis & Hayes, 2012). After applying this correction for missing items, between .6% and 1.6% were missing data for each subscale, with complete FFMQ data available for 844 of 867 participants. Zero order correlations were conducted to examine the relationship between mindfulness facets and symptoms of depression and anxiety.
Analysis of Covariance (ANCOVA) was used to examine differences in scores on mindfulness facets between patients with a current/partial remission SUD, past history of SUD, or no history of SUD. SUD status was based on presence of alcohol and/or illicit drug use disorders. Cigarette smoking was not counted as a SUD (it was analyzed separately). It is important to note that patients without a SUD history were diagnosed with other psychiatric disorders (see Table 1). The CGI depression, somatic anxiety, and psychic anxiety ratings from clinical interviews were entered as covariates to control for the relationship between mindfulness and depression/anxiety symptoms (e.g., Cash & Wittingham, 2010). Post hoc tests were conducted using the Tukey least significant difference test to examine differences in mindfulness facet scores between these three diagnostic groups. A multinomial logistic regression was then conducted to further examine the degree to which facets of mindfulness predict the presence of a current SUD, history of SUD and no history of SUD, when controlling for depression and anxiety variables. The multinomial logistic regression also provided a means of examining the relationship of each mindfulness facet to SUD status while controlling for other facets of mindfulness to better determine their independent relationship to SUDs.
Follow-up ANCOVA and multinomial logistic regression analyses were conducted separately with alcohol use disorders and with illicit drug use disorders. Additional analyses examined whether mindfulness facets differed between current smokers, past smokers, and patients with no history of smoking using ANCOVA and regression analyses. SUD status was included as an additional covariate in smoking analyses given the comorbidity between SUDs and cigarette smoking.
Results Zero Order Correlations
Analyses were conducted to examine the relationships among the mindfulness facets and between these facets and symptoms of depression and anxiety (see Table 2). The describing, acting with awareness, nonjudgmental, and nonreactivity facets were all significantly correlated in the expected direction (i.e., greater mindfulness in one facet relating to greater mindfulness in another facet) with correlation coefficients ranging from .18 to .40. The observing facet was correlated with each of the other facets, but the correlations with acting with awareness and being nonjudgmental were in the opposite direction (i.e., greater observing related to lower acting with awareness and being nonjudgmental).
Zero Order Correlations Between Mindfulness Facets, Depression Symptoms, and Anxiety Symptoms
Depression and anxiety symptoms were significantly correlated with describing, acting with awareness, nonjudgmental, and nonreactivity facets such that greater mindfulness related to lower symptoms, with correlation coefficients ranging from .10 to .40. However, the observing facet was unrelated to depression and correlated in the opposite direction with anxiety symptoms such that higher observing related to greater anxiety.
Analyses With Past and Current SUD Diagnoses
Analyses were conducted to examine differences in facets of mindfulness by diagnostic groups. Descriptive statistics for each FFMQ subscale by diagnostic group are provided in Table 3. ANCOVA results indicated a significant main effect for SUD status with the FFMQ acting with awareness subscale, F(2, 853) = 9.71, p < .001, partial η2 = .02, nonjudgmental subscale, F(2, 853) = 4.32, p = .01, partial η2 = .01, and nonreactivity subscale, F(2, 845) = 5.27, p = .005, partial η2 = .01. There were no significant main effects for SUD status on the observing or describing subscales (p > .10).
Descriptive Statistics for Each Mindfulness Facet by Diagnostic group
Pairwise comparisons indicated significantly higher scores on acting with awareness among patients with no history of SUD relative to those with current SUD (covariate adjusted mean [Mdiff] = 2.46, SE = .61, p < .001) and relative to those with a past SUD history (Mdiff = 1.49, SE = .52, p = .004). Patients with no history of SUD had significantly higher levels on being nonjudgmental relative to those with current SUD (Mdiff = 1.82, SE = .63, p = .004), but there were no significant differences between patients with a past history of SUD and the other two groups. Patients with current SUD had significantly lower levels of nonreactivity relative to those with no history of SUD (Mdiff = 1.42, SE = .46, p = .002) and relative to patients with a past history of SUD (Mdiff = 1.41, SE = .50, p = .005).
A multinomial logistic regression was conducted to further examine which mindfulness facets predicted having a current SUD, past history of SUD, or no history of SUD, when controlling for the other mindfulness facets as well as depression and anxiety symptoms (see Table 4). The overall model significantly predicted SUD status, with post hoc analyses indicating acting with awareness, nonreactivity, and being nonjudgmental as predictors of having a current SUD relative to no history of SUD. Only the acting with awareness subscale significantly predicted having a history of SUD relative to no history of SUD. There was also a statistical trend for depression predicting a history of SUD relative to no history of SUD. In each case lower mindfulness and greater symptom scores were predictive of a greater likelihood of having a SUD diagnosis relative to no history of SUD. No other facets of mindfulness or anxiety symptoms were predictive of SUD status (p > .10).
Multinomial Logistic Regression Results Testing Mindfulness and Depression and Anxiety Symptoms as Predictors of SUD
Follow-Up Analyses With Alcohol Use Disorders
Follow up analyses were conducted separately among patients with alcohol use disorders (current diagnosis n = 94; past diagnosis n = 234) relative to patients with no SUD history (i.e., no history of alcohol use or drug use disorders; n = 471). ANCOVA results indicated a significant main effect for alcohol use disorder status with the FFMQ describing, F(2, 793) = 3.17, p = .043, partial η2 = .01, acting with awareness, F(2, 792) = 9.49, p < .001, partial η2 = .02, being nonjudgmental, F(2, 792) = 5.19, p = .006, partial η2 = .01, and nonreactivity subscales, F(2, 785) = 3.11, p = .045, partial η2 = .01. There was no significant main effect for SUD status on the observing subscale (p > .10).
Post hoc pairwise comparisons indicated significantly higher scores on describing among patients with no history of SUD relative to those with a past alcohol disorder history (Mdiff = 1.32, SE = .53, p = .013), but no difference between current alcohol disorder and either past or no SUD history (p > .10). Patients with no history of SUD had significantly higher scores on acting with awareness relative to those with a current alcohol disorder (Mdiff = 2.43, SE = .74, p = .001) and relative to patients with a past alcohol disorder (Mdiff = 1.89, SE = .53, p < .001). Patients with a current alcohol disorder had significantly lower scores on being nonjudgmental relative to those with no history of SUD (Mdiff = 2.39, SE = .76, p = .002) and relative to patients with a past alcohol disorder (Mdiff = 1.63, SE = .82, p = .046). Patients with a current alcohol disorder had significantly lower scores on nonreactivity relative to those with no history of SUD (Mdiff = 1.35, SE = .54, p = .013) and a trend relative to patients with a past alcohol disorder (Mdiff = 1.04, SE = .59, p = .076).
The overall model was significant for the multinomial logistic regression examining mindfulness facets and depression and anxiety symptoms as predictors of current alcohol disorder, past alcohol disorder or no SUD history (see Table 4). Post hoc analyses indicated that acting with awareness, being nonjudgmental, and nonreactivity predicted having a current alcohol use disorder relative to no history of SUD. Only the acting with awareness subscale significantly predicted having a history of alcohol disorder relative to no history of SUD. In each case lower mindfulness scores were predictive of a greater likelihood of having an alcohol disorder diagnosis relative to no history of SUD. No other facets of mindfulness or depression and anxiety symptoms were predictive of alcohol disorder status (p > .10).
Follow-Up Analyses With Drug Use Disorders
Follow-up analyses were conducted separately among patients with illicit drug use disorders (current diagnosis n = 77; past diagnosis n = 152) relative to patients with no SUD history (i.e., no history of alcohol use or drug use disorders; n = 471). ANCOVA results indicated a significant main effect for drug use disorder status with the FFMQ describing, F(2, 695) = 3.59, p = .028, partial η2 = .01, acting with awareness, F(2, 694) = 9.29, p < .001, partial η2 = .03, being nonjudgmental, F(2, 693) = 4.30, p = .014, partial η2 = .01, and nonreactivity subscales, F(2, 685) = 3.17, p = .043, partial η2 = .01. There was no significant main effect for SUD status on the observing subscale (p > .10).
Post hoc pairwise comparisons indicated significantly higher scores on describing among patients with no history of SUD relative to those with a current drug disorder (Mdiff = 2.10, SE = .82, p = .010), but no difference between past drug disorder and either current or no SUD history (p > .10). Patients with no history of SUD had significantly higher scores on acting with awareness relative to those with a current drug disorder (Mdiff = 3.01, SE = .81, p < .001) and relative to patients with a past drug disorder (Mdiff = 1.75, SE = .62, p = .005). Patients with no history of SUD had significantly higher scores on being nonjudgmental relative to those with a current drug disorder (Mdiff = 1.98, SE = .83, p = .017) and relative to patients with a past drug disorder (Mdiff = 1.33, SE = .63, p = .036). Patients with a current drug disorder had significantly lower scores on nonreactivity relative to those with no history of SUD (Mdiff = 1.51, SE = .60, p = .012) and relative to patients with a past drug disorder (Mdiff = 1.35, SE = .69, p = .050).
The overall model was significant for the multinomial logistic regression examining mindfulness facets and depression and anxiety symptoms as predictors of current drug disorder, past drug disorder, or no SUD history (see Table 4). Post hoc analyses indicated that acting with awareness and nonreactivity predicted having a current drug disorder relative to no history of SUD. Only the acting with awareness subscale significantly predicted having a history of drug disorder relative to no history of SUD. In each case lower mindfulness scores were predictive of a greater likelihood of having a drug disorder diagnosis relative to no history of SUD. No other facets of mindfulness or depression and anxiety symptoms were predictive of drug disorder status (p > .10).
Follow-Up Analyses With Smoking Status
A series of analyses were conducted to examine potential deficits in mindfulness facets among patients who reported currently smoking cigarettes (n = 202), a past history of regularly smoking (n = 245), or no history of regularly smoking (n = 419). ANCOVA analyses controlling for depression and anxiety symptoms as well as SUD status indicated a significant main effect for smoking status with the FFMQ nonjudgmental subscale, F(2, 850) = 5.06, p = .007, partial η2 = .01. There was no significant main effect for smoking status on the observing, describing, acting with awareness, or nonreactivity subscales (p > .10). Post hoc pairwise comparisons indicated significantly lower scores on being nonjudgmental among current smokers relative to past smokers (Mdiff = 2.04, SE = .64, p = .002), and a trend for lower scores among current smokers relative to patients with no history of smoking (Mdiff = 1.18, SE = .61, p = .052).
The overall model was significant for the multinomial logistic regression examining mindfulness facets, depression and anxiety symptoms, and SUD status as predictors of current smoking, past smoking, or no smoking history (see Table 4). Post hoc analyses indicated a trend for describing and being nonjudgmental predicting being a current smoker relative to no history of smoking. SUD status predicted being a current smoker relative to no history as well as being a past smoker relative to no history. In each case lower mindfulness scores and history of SUD were predictive of a greater likelihood of being a smoker relative to no history.
DiscussionThe current study sought to examine the relationship between facets of mindfulness and SUDs in a clinical sample. Results indicated that deficits in acting with awareness, being nonjudgmental, and nonreactivity were related to the presence of a current SUD relative to no history of SUD. In addition, patients with a past history of SUD had deficits in acting with awareness relative to those with no history of SUD. When controlling for other mindfulness facets in a multinomial logistic regression, only the acting with awareness and nonreactivity subscales significantly predicted presence of a current SUD relative to no history of SUD. A similar pattern of results was found when examining alcohol use and drug use disorders separately, except that describing was also related to SUD status. Current cigarette smokers demonstrated deficits in being nonjudgmental relative to past smokers and those with no history of smoking, but did not differ on other mindfulness facets. The observing facet was not related to current or past history of SUD in any analyses.
These results add to the existing literature suggesting that specific facets of mindfulness are related to SUDs, particularly deficits in acting with awareness, being nonjudgmental, and nonreactivity (Fernandez et al., 2010; Leigh & Neighbors, 2009; Murphy & MacKillop, 2012). In addition, the results suggest that patients who smoke may have elevated deficits in being nonjudgmental of their emotions, which adds to research indicating the role of mindfulness and emotional acceptance in nicotine dependence (e.g., Gifford et al., 2011; Vidrine et al., 2009). This is the first study we are aware of that has examined these relationships in nonintervention research using a clinical sample of psychiatric patients, suggesting that similar findings generalize to clinical populations.
Theoretically, the tendency to act without awareness could contribute to habitually engaging in addictive behaviors as well as failing to recognize and thus effectively cope with triggers and high risk situations that lead to relapse. The tendency to judge and be reactive to difficult emotions, thoughts, and urges may also elicit maladaptive avoidant behavior including further substance use, which is consistent with research indicating the relationship of distress tolerance and emotion regulation to SUDs (Aldao et al., 2010; Leyro et al., 2010). Experimental research is needed to further explore the role of these mindfulness facets in SUDs.
Consistent with past research, deficits in the observing facet was not related to SUD status. However, the study also did not find a positive relationship between heightened observation of the present moment and increased substance abuse, which has been found in some past research (Leigh et al., 2005; Leigh & Neighbors, 2009). Theoretically, heightened observation could be a protective or risk factor depending on skills in other mindfulness facets. For example, if one was highly judgmental and reactive to one’s experiences, heightened observation could be indicative of hyperarousal and problematic self-monitoring. Alternatively, heightened observation in the context of being nonjudgmental and nonreactive could reflect an enhanced mindful attention to the present. Consistent with this, a recent study found a significant interaction between mindful observation and nonreactivity in predicting substance use, such that individuals who were high in observation and nonreactivity were less likely to use substances, whereas those high in observation and low in nonreactivity were more likely to use substances (Eisenlohr-Moul et al., 2012).
Although these results do not indicate whether increasing mindfulness would be beneficial for addiction problems, they are consistent with other research which suggests that targeting present moment awareness and the capacity to notice experiences without judging or impulsively reacting to them may reduce substance use problems. For example, a recent study found that a mindfulness-based intervention for substance abuse reduced the occurrence of cravings in reaction to feeling depressed, and that this effect predicted lower rates of substance abuse (Witkiewitz & Bowen, 2010). Another study found that a mindfulness-based intervention for substance abuse reduced the tendency to try to suppress thoughts, which was related to better addiction outcomes (Bowen, Witkiewitz, Dilworth & Marlatt, 2007). These findings suggest that increasing one’s ability to mindfully notice one’s experiences in the present moment, without judging them or reacting to them, may be a promising mechanism for treating addictions. Yet, it is important to note that reviews of the literature indicate methodological weaknesses with existing research on mindfulness-based interventions for SUDs and the need for more research (Chiesa & Serretti, 2013).
There are a few key limitations with the current study. First, the study used a cross sectional design so the temporal relationship between mindfulness deficits and SUD status is unclear. Although it is possible that mindfulness deficits may lead to substance abuse, chronic substance abuse may also reduce one’s present moment awareness and increase reactivity to difficult thoughts and feelings. Future research would benefit from longitudinal designs that can more clearly determine whether mindfulness deficits are risk factors for the development and exacerbation of SUDs.
Given the services provided at the outpatient treatment program where the MIDAS Project is conducted, only 17% of the patients with a current or past SUD had a SUD diagnosis alone. There is a high prevalence of comorbid psychiatric diagnoses among SUD populations (Conway, Compton, Stinson & Grant, 2006; Kessler, 2004), which lends to the generalizability of the current findings. Nonetheless, it is unclear whether the observed deficits in mindfulness facets are attributable to the presence of a SUD in general or unique to features regarding having a comorbid SUD in addition to other psychiatric disorders. However, we statistically controlled for depression and anxiety symptoms, reducing the potential that mindfulness deficits were attributable to these comorbid symptoms. Individuals with comorbid psychiatric diagnoses and SUDs tend to be more severe and difficult to treat (Hawkins, 2009; Kessler, 2004), and mindfulness-based interventions have been found to be efficacious for a broad range of psychological problems (Hayes, Villatte, Levin & Hildebrandt, 2011). Mindfulness-based interventions may be applicable to individuals with comorbid SUD and other psychiatric diagnoses, which is a population particularly in need of innovative treatments (O’Brien et al., 2004). To better understand the relationship of mindfulness deficits to SUD and comorbid populations, further research would benefit from examining whether similar mindfulness deficits are observed among individuals with SUD alone relative to those with other psychiatric diagnoses without SUD as well as comorbid SUD and psychiatric diagnoses.
The current study also did not include measures assessing related constructs that may better account for SUDs or the relationship between SUDs and mindfulness. For example, a recent study found that the relationship between mindfulness and SUDs was fully accounted for by impulsivity (Murphy & MacKillop, 2012). It is unclear whether mindfulness provides unique predictive utility over and above other established constructs such as impulsivity (Murphy & MacKillop, 2012), distress tolerance (Leyro et al., 2010), and emotion regulation (Gratz & Roemer, 2004). Continuing to examine the unique role of mindfulness facets in relation to these other variables could help to better understand risk factors and potential mechanisms of change for SUDs.
The FFMQ limited the facets of mindfulness that were examined in relation to SUDs. It may be the case that other mindfulness facets, such as the acceptance subscale from the Philadelphia Mindfulness Scale (Cardaciotto et al., 2008), would have demonstrated different relationships with SUDs. In addition, previous research has indicated that the observing subscale of the FFMQ tends to relate to greater distress (e.g., Baer et al., 2006), and it is unclear whether a similar relationship would be found with another present moment awareness mindfulness subscale. Future research should include a more comprehensive range of mindfulness measures in addition to related constructs.
The current study combined substance abuse and dependence diagnostic groups. Although this served to increase statistical power to detect differences in mindfulness facets, it also may raise questions regarding heterogeneity within the SUD groups. However, previous research has found minimal value in distinguishing the abuse and dependence diagnostic groups and has led to combining these two categories in the DSM-5 (Hasin, 2012; O’Brien, 2011).
It is important to note, however, that the effect sizes for differences in mindfulness facets across groups were small with partial eta squared values of .01 to .02. This may be attributable in part to the use of a multifaceted measure in which the variance between groups in mindfulness may have been partitioned across multiple subscales, thus indicating small effect sizes for each variable. In addition, the comparison group of psychiatric patients was likely to already have deficits in mindfulness facets given the relationship between mindfulness and psychological symptoms (e.g., Cash & Wittingham, 2010), which may have further reduced effect sizes. Future research would benefit from examining whether these findings replicate and whether larger effect sizes may be observed relative to nonclinical control groups.
This study adds to a growing body of literature suggesting the potential role of specific mindfulness facets in addictions (Eisenlohr-Moul et al., 2012; Fernandez et al., 2010). Given the multifaceted nature of mindfulness, future research would benefit from continuing to identify which facets, if any, are functionally important in addictions and what intervention methods can best target these processes. This research may eventually serve to inform more efficient, effective, and focused interventions targeting specific facets of mindfulness.
Footnotes 1 Between 2.2% and 8.7% of participants were missing one or more items for each FFMQ subscale. The reported results were similar when only analyzing the sub-sample who completed all items for a given FFMQ subscale.
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Submitted: January 31, 2013 Revised: June 11, 2013 Accepted: August 28, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 498-506)
Accession Number: 2013-40799-001
Digital Object Identifier: 10.1037/a0034706